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Large-Scale Structure beyond the Power Spectrum

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Marcel Schmittfull

As recent and future galaxy surveys map the large-scale structure of the universe with unprecedented pace and precision, it is worthwhile to consider innovative data analysis methods beyond traditional Gaussian 2-point statistics to extract more cosmological information from those datasets. Such efforts are often plagued by substantially increased complexity of the analysis. Hoping to improve this, I will present simple, nearly optimal methods to measure 3-point statistics as easily as 2-point statistics, by cross-correlating the mass density with specific quadratic fields [arXiv:1411.6595]. Inspired by these results, I will argue that BAO reconstructions already combine 2-point statistics with certain 3- and 4-point functions automatically [arXiv:1508.06972]. I will present several new Eulerian and Lagrangian reconstruction algorithms and discuss their performance in simulations.

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LARGE-SCALE STRUCTURE! BEYOND! THE POWER SPECTRUM Marcel Schmittfull! ! Berkeley Center for Cosmological Physics (BCCP)! ! arxiv:1411.6595 (PRD 91, 043530) ! arxiv:1508.06972! ! 
 In collaboration with! Tobias Baldauf, Uros Seljak
 Yu Feng, Florian Beutler, Blake Sherwin, Man Yat Chu! ! Berkeley BCCP Seminar, Sep 8 2015
Introduction! Part I: Simple bispectrum estimators! Part II: Eulerian reconstructions and N-point statistics OVERVIEW
INTRODUCTION
MAIN COSMOLOGICAL PROBES Young universe (380,000 yrs after big bang)! [Gravity waves] ! Cosmic Microwave Background (CMB)! ! ‘Recent’ universe (billions yrs after big bang)! Large-scale structure (LSS)! 21cm, Lyα, CMB lensing, galaxy clustering, weak lensing! Supernovae All observations can be described with LCDM cosmological model 
 (start with inflation, then expand with CDM and cosmological constant dark energy) Dana Berry / SkyWorks Digital Inc. and the SDSS collaboration
MAIN COSMOLOGICAL PROBES Constraints on 6-parameter LCDM model:! CMB is most powerful! 24 Florian Beutler et al. mΩ 0.1 0.2 0.3 0.4 0.5 0.6 0.7 8 σ 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 CDM)ΛPlanck ( )8 σCMASS (f Our analysis )8 σ,f AP CMASS (F Our analysis Planck SZ clusters Ade et al. (2013) CFHTLenS Kilbinger et al. (2013) )8 σ,f AP ,Fs/rv CMASS (D s Planck Our analysis + r )8 σ,f AP ,Fs/rv CMASS (D s WMAP9 Our analysis + r Figure 17. Comparison between Planck (Ade et al. 2013a), Beutler et al 2013 DM fraction of total energy budget in the universe Magnitudeoffluctuations
MAIN COSMOLOGICAL PROBES Constraints on 6-parameter LCDM model:! CMB is most powerful! Free neutrino mass Σmν! Constraints degrade because Σmν and σ8 degenerate in CMB! LSS helps DM fraction of total energy budget in the universe Magnitudeoffluctuations 10 Florian Beutler et al. mΩ 0.25 0.3 0.35 0.4 0.45 0.5 8 σ 0.65 0.7 0.75 0.8 0.85 )νmΣPlanck (free )νmΣPlanck+CMBlensing (free CFHTLenS Kilbinger et al. (2013) Planck+Beutler2013 Planck+Beutler2013+CFHTLenS Beutler et al 2013
MAIN COSMOLOGICAL PROBES Constraints on 6-parameter LCDM model:! CMB is most powerful! Free neutrino mass Σmν! Constraints degrade because Σmν and σ8 degenerate in CMB! LSS helps! Sum of neutrino masses Magnitudeoffluctuations utler et al. mΩ 0.35 0.4 0.45 0.5 )νmΣPlanck (free )νmΣPlanck+CMBlensing (free CFHTLenS Kilbinger et al. (2013) Planck+Beutler2013 Planck+Beutler2013+CFHTLenS [eV]νmΣ 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 8σ 0.65 0.7 0.75 0.8 0.85 )νmΣPlanck (free Planck+Beutler2013 Planck+Beutler2013+CFHTLenS Beutler et al 2013
MAIN COSMOLOGICAL PROBES Constraints on 6-parameter LCDM model:! CMB is most powerful! Free neutrino mass Σmν! Constraints degrade because Σmν and σ8 degenerate in CMB! LSS helpful! Other model extensions (curvature, time-varying dark energy, mod. grav. etc)! LSS crucial Beutler et al 2013 Dana Berry / SkyWorks Digital Inc. and the SDSS collaboration Generally, should not just compare constraining power: LSS checks cosmological model as a whole (low z vs high z from CMB)
STATISTICAL! PROPERTIES! OF LSS
@⌘ + r[(1 + )v)] = 0 Equations of motion for DM are non-linear, containing products of Gaussian first- order perturbations (e.g. continuity equation )! ➟ DM density is not Gaussian (even in absence of primordial non-Gaussianity): kmax ~ 0.15 h/Mpc, z = 0.55, 20483 DM ptcles DARK MATTER DENSITY
NON-GAUSSIANITY FROM GRAVITY Another complication:! Distribution of dark matter is different from that of galaxies ➟ “Galaxy bias”! ! !
NON-GAUSSIANITY FROM GRAVITY Another complication:! Distribution of dark matter is different from that of galaxies ➟ “Galaxy bias”! ! ! night human population density Adopted from Tobias Baldauf
NON-GAUSSIANITY FROM GRAVITY Another complication:! Distribution of dark matter is different from that of galaxies ➟ “Galaxy bias”! ! ! night human population density Adopted from Tobias Baldauf
Galaxies are biased tracer of DM! Bias relation has additional non-linearities! ➟ pdf of galaxy density is even more non-Gaussian than that for DM: kmax ~ 0.15 h/Mpc, z = 0.55, halos with b1~2 and mass 1-4 x 1013 Msolar/h GALAXY DENSITY g(x) ⇠ b1 m(x) + b2 2 m(x) + bs2 s2 m(x)
QUANTIFYING! THE PDF
Gaussian field is completely specified by its power spectrum Pδ 
 (2-point correlation function in Fourier space)
 
 
 
 
 
 Primary diagnostic for non-Gaussianity is the bispectrum Bδ 
 (3-point correlation function in Fourier space:
 given 2 over-densities, specifies probability of having a third overdensity)! ! ! ! ! POWER AND BISPECTRUM h (k1) (k2)i = (2⇡)3 D(k1 + k2)P (k1) bispectrum drawn on space of triangle configurations = (2⇡)3 D(k1 + k2 + k3)B (k1, k2, k3) h (k1) (k2) (k3)i 2.4 Large-scale dark matter perturbations 10 3 10 2 10 1 100 k [Mpc 1] 101 102 103 104 105 P(k,z=0)[Mpc3] SDSS DR7 (Reid et al. 2010) LyA (McDonald et al. 2006) ACT CMB Lensing (Das et al. 2011) ACT Clusters (Sehgal et al. 2011) CCCP II (Vikhlinin et al. 2009) BCG Weak lensing (Tinker et al. 2011) ACT+WMAP spectrum (this work) 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 Mass scale M [Msolar] 10 6 10 5 10 4 10 3 10 2 10 1 100 101 102 MassVarianceM/M SDSS DR7 (Reid et al. 2010) LyA (McDonald et al. 2006) ACT CMB Lensing (Das et al. 2011) ACT Clusters (Sehgal et al. 2011) CCCP II (Vikhlinin et al. 2009) BCG Weak lensing (Tinker et al. 2011) ACT+WMAP spectrum (this work) Fig. 5.— The reconstructed matter power spectrum: the stars show the power spectrum from combining ACT and WMAP data (to panel). The solid and dashed lines show the nonlinear and linear power spectra respectively from the best-fit ACT ⇤CDM model wit spectral index of ns = 0.96 computed using CAMB and HALOFIT (Smith et al. 2003). The data points between 0.02 < k < 0.19 Mpc show the SDSS DR7 LRG sample, and have been deconvolved from their window functions, with a bias factor of 1.18 applied to the dat This has been rescaled from the Reid et al. (2010) value of 1.3, as we are explicitly using the Hubble constant measurement from Riess et a (2011) to make a change of units from h 1Mpc to Mpc. The constraints from CMB lensing (Das et al. 2011), from cluster measurement from ACT (Sehgal et al. 2011), CCCP (Vikhlinin et al. 2009) and BCG halos (Tinker et al. 2011), and the power spectrum constrain from measurements of the Lyman–↵ forest (McDonald et al. 2006) are indicated. The CCCP and BCG masses are converted to solar ma units by multiplying them by the best-fit value of the Hubble constant, h = 0.738 from Riess et al. (2011). The bottom panel shows th (Hlozek et al. 2011) ~ keq Figure 2.8: Reconstructed matter power spectrum today, adapted from Fig. 5 in [117]. The data points are from various large-scale structure and CMB ex- periments. The dashed line represents the linear power spectrum which follows approximately (2.120). The solid line contains non-linear corrections which are important on small scales. The integral can be computed numerically using the Friedmann equation ✓ H(a) H0 ◆2 = ⌦ra 4 + ⌦ma 3 + ⌦⇤ + ⌦ka 2 , (2.122) where ⌦X are the density parameters today (when a = 1). Note that we only consider the fastest growing mode here and in the following. 2.4.4 Higher-order perturbation theory The late-time dark matter distribution has a non-trivial bispectrum even in the absence of primordial non-Gaussianity due to non-linearities in the equations of 59 Hlozek et al. 2012
MOTIVATION FOR MEASURING BISPECTRUM Early universe: Constrain primordial non-Gaussianity! CMB close to cosmic variance limit, so need LSS ! : Bispectrum complementary to P(k) and less affected by low-k systematics! : No signal in P(k) → need bispectrum! e.g. SPHEREX (proposed satellite) floc NL feq NL Late universe: Break parameter degeneracies, e.g. between b1 and σ8:! Power spectrum: ! Bispectrum: ! Lots of upcoming LSS experiments that could benefit, e.g. eBOSS, DESI, EUCLID, WFIRST, LSST, … Phh / b2 1 2 8 Bhhh /⇠ b3 1 4 8+ ⇠ b2 1b2 4 8 + · · · Fry 1994! Verde et al. 1997-2002! Scoccimarro et al. 1998! Sefusatti et al. 2006! Gil-Marin et al. 2014 Doré et al. 2014
MOTIVATION FOR MEASURING BISPECTRUM Early universe: Constrain primordial non-Gaussianity! CMB close to cosmic variance limit, so need LSS ! : Bispectrum complementary to P(k) and less affected by low-k systematics! : No signal in P(k) → need bispectrum! e.g. SPHEREX (proposed satellite) floc NL feq NL Late universe: Break parameter degeneracies, e.g. between b1 and σ8:! Power spectrum: ! Bispectrum: ! Lots of upcoming LSS experiments that could benefit, e.g. eBOSS, DESI, EUCLID, WFIRST, LSST, … Phh / b2 1 2 8 Bhhh /⇠ b3 1 4 8+ ⇠ b2 1b2 4 8 + · · · Fry 1994! Verde et al. 1997-2002! Scoccimarro et al. 1998! Sefusatti et al. 2006! Gil-Marin et al. 2014 But: Beyond 2-point plagued by increased complexity of analysis
 (covariances, window function, fiber collisions, computational cost ~ N6; 
 non-linear DM, bias, redshift-space distortions, galaxy-halo connection, …) Doré et al. 2014
SIMPLE BISPECTRUM ESTIMATORS
Non-linear DM density (2nd order SPT)! ! ! ! ! ! ! ! THEORY DM BISPECTRUM m(x) = 0(x) | {z } linear + 17 21 2 0(x) | {z } nonlinear growth + 0(x) · r 0(x) | {z } shift + 4 21 s2 0(x) | {z } tidal ) h m(k1) m(k2) m(k3)i ⇠ 2Plin mm(k1)Plin mm(k2)F2(k1, k2) + 2 perms in k1, k2, k3 F2(k1, k2) = 17 21|{z} nonlinear growth + 1 2 ✓ k1 k2 + k2 k1 ◆ ˆk1 · ˆk2 | {z } shift + 4 21 3 2 ✓ (ˆk1 · ˆk2)2 1 3 ◆ | {z } tidal δm: nonlinear DM density
 δ0: linear DM density
 : linear displacement
 s0: linear tidal tensor ⌘ ik k2 0(k)0 where
Non-linear halo bias! ! ! ! ! ! ! ! THEORY HALO BISPECTRUM δm: nonlinear DM density
 δh: halo density
 sm: DM tidal tensor h(x) = b1 m(x) + b2 ⇥ 2 m(x) h 2 m(x)i ⇤ + 2 3 bs2 ⇥ s2 m(x) hs2 m(x)i ⇤ ) h h(k1) h(k2) h(k3)i ⇠ 2Plin mm(k1)Plin mm(k2)  b3 1F2(k1, k2) + b2 1b2 + 2 3 b2 1bs2 P2(ˆk1 · ˆk2) + 2 perms
Non-linear halo bias! ! ! ! ! ! ! ! THEORY HALO BISPECTRUM δm: nonlinear DM density
 δh: halo density
 sm: DM tidal tensor h(x) = b1 m(x) + b2 ⇥ 2 m(x) h 2 m(x)i ⇤ + 2 3 bs2 ⇥ s2 m(x) hs2 m(x)i ⇤ ) h h(k1) h(k2) h(k3)i ⇠ 2Plin mm(k1)Plin mm(k2)  b3 1F2(k1, k2) + b2 1b2 + 2 3 b2 1bs2 P2(ˆk1 · ˆk2) + 2 perms Decompose in Legendre polys! ! ! ! ! ! ! ! Pl with l = 0, 1, 2 B (l=0) hhh = ✓ 34 21 b3 1 + 2b2 1b2 ◆ Plin mm(k1)Plin mm(k2) + 2 perms, B (l=1) hhh = b3 1 ✓ k1 k2 + k2 k1 ◆ Plin mm(k1)Plin mm(k2)P1(ˆk1 · ˆk2) + 2 perms, B (l=2) hhh = ✓ 8 21 b3 1 + 4 3 b2 1bs2 ◆ Plin mm(k1)Plin mm(k2)P2(ˆk1 · ˆk2) + 2 perms.
BISPECTRUM ESTIMATION Goal: Given DM/halo density δ, estimate coefficients of all bispectrum contributions! ➟ these depend on bias and cosmological parameters that we aim to extract! Method: Maximum likelihood estimators for the coefficient of contribution Bcontri
 
 
 
 
 Example: Bcontri = P(k1)P(k2) 
 
 
 
 " ! ! ! ˆcoe↵(Bcontri | ) / Z k,q Bcontri (q, k q, k) | {z } theory template (q) (k q) ( k) P (q)P (|k q|)P (k) | {z } inv. variance weighted data MS, Baldauf, Seljak, 1411.6595 = = cross-spectrum of δ2(x) and δ! ) ˆcoe↵ / Z dk k2 P(k) ˆP 2, (k) 0 h 2 | i0 ˆP 2, (k) ⇠ X k,|k|=k [ 2 ](k) ( k) relies on separability of! LSS bispectrum
MS, Baldauf, Seljak, 1411.6595 l=0
 growth • nonlinear DM growth δ2! • quadratic bias b2 squared density X density ˆP 2, (k) ⇠ X k,|k|=k [ 2 ](k) ( k) • nonlinear DM shift (x) · r (x) i (k) ⌘ iki k2 (k) l=1
 shift displacement dot
 density gradient X density ˆP i@i , (k) ⇠ X k,|k|=k [ i @i ](k) ( k) A. Qua As will be shown in Section III, maximum-likelihood form of cross-spectra of the density field with three field dependencies on the cosine µ between the Fourier space µ ⌘ q Explicitly, these three quadratic fields are: • The squared density 2 (x), which can be written a 2 (x) = Z d3 k (2⇡)3 eik where P0(µ) = 1 is the Legendre polynomial for l = • The shift-term i (x)@i (x) = (x) · r (x) = Z which depends on the l = 1 Legendre polynomial P r with the displacement field (k The symmetric kernel F1 2 in Eq. (3) is defined as F1 2 (k1, k2 • The tidal term3 s2 (x) ⌘ 3 2 sij(x)sij(x) = Z which is defined by contracting the tidal tensor sij(k) = ✓ with itself. (K) denotes the Kronecker delta. The dependencies on the cosine µ between the Fourier space wavevectors q and k q µ ⌘ q · (k q) q|k q| . Explicitly, these three quadratic fields are: • The squared density 2 (x), which can be written as a convolution in Fourier 2 (x) = Z d3 k (2⇡)3 eikx Z d3 q (2⇡)3 P0(µ) (q) (k where P0(µ) = 1 is the Legendre polynomial for l = 0.2 • The shift-term i (x)@i (x) = (x) · r (x) = Z d3 k (2⇡)3 eikx Z d3 q (2⇡)3 F1 2 (q, |k which depends on the l = 1 Legendre polynomial P1(µ) = µ and is obtained r with the displacement field (k) = ik k2 (k). The symmetric kernel F1 2 in Eq. (3) is defined as F1 2 (k1, k2) = 1 2 ✓ k1 k2 + k2 k1 ◆ . • The tidal term3 s2 (x) ⌘ 3 2 sij(x)sij(x) = Z d3 k (2⇡)3 eikx Z d3 q (2⇡)3 P2(µ) which is defined by contracting the tidal tensor sij(k) = ✓ kikj k2 1 3 (K) ij ◆ (k) with itself. (K) ij denotes the Kronecker delta. The corresponding convolutio l = 2 Legendre polynomial • nonlinear DM tidal term! • tidal tensor bias bs2 l=2
 tidal ˆPs2, (k) ⇠ X k,|k|=k [s2 ](k) ( k) tidal tensor X density GET ALL BISPECTRUM CONTRIS 
 FROM 3 CROSS-SPECTRA
IN PICTURES MS, Baldauf, Seljak, 1411.6595 1. Start with density! (x)y[Mpc/h] x [Mpc/h]
IN PICTURES MS, Baldauf, Seljak, 1411.6595 y[Mpc/h] x [Mpc/h] R(x) 2. Apply smoothing (here Gaussian with R=20 Mpc/h)!
IN PICTURES MS, Baldauf, Seljak, 1411.6595 y[Mpc/h] x [Mpc/h] 2 R(x) 3. Get squared density!
IN PICTURES MS, Baldauf, Seljak, 1411.6595 y[Mpc/h] x [Mpc/h] R(x) · r R(x) 4. Get shift term!
IN PICTURES MS, Baldauf, Seljak, 1411.6595 y[Mpc/h] x [Mpc/h] s2 R(x) 5. Get tidal term
l=0
 growth • nonlinear DM 
 growth δ2! • quadratic bias b2 squared density density ˆP 2, (k) ⇠ X k,|k|=k [ 2 ](k) ( k) • nonlinear DM shift (x) · r (x) l=1
 shift displacement dot
 density gradient density ˆP i@i , (k) ⇠ X k,|k|=k [ i @i ](k) ( k) • nonlinear DM 
 tidal term! • tidal tensor bias l=2
 tidal ˆPs2, (k) ⇠ X k,|k|=k [s2 ](k) ( k) tidal tensor density IN PICTURES 6. Cross-correlate MS, Baldauf, Seljak, 1411.6595 X X X ⇠ ⇠ ⇠ k [Mpc/h] k [Mpc/h] k [Mpc/h]
PRIMORDIAL NON-GAUSSIANITY Optimal estimator from DM bispectrum: MS, Baldauf, Seljak, 1411.6595 VI. EXTENSION TO PRIMORDIAL NON-GAUSSIANITY So far we have assumed Gaussian initial conditions. Multiple-field inflation models can generate loc non-Gaussianity that induces an additional contribution to the matter-matter-matter bispectrum of th Bloc mmm(k1, k2, k3) = 2floc NL  M(k3) M(k1)M(k2) Pmm(k1)Pmm(k2) + 2 perms , where M(k) = M(k, z) is the linear Poisson conversion factor between the primordial potential and matter density at redshift z, M(k, z) ⌘ 2 3 k2 T(k)D(z) ⌦mH2 0 , so that lin m (k, z) = M(k, z) (k). Here, T(k) is the linear transfer function normalized to T(k) = 1 on and the linear growth factor D(z) for ⌦rad = 0 is normalized to D(z) = 1/(1 + z) during matter dom that M(k) / k2 for k ⌧ keq and M(k) / k0 for k keq. The bispectrum (82) is maximal in the s (e.g. k1 ⌧ k2 ⇡ k3). Plugging the bispectrum (82) into Eq. (36), we get ˆfloc NL = 24⇡L3 Nloc Z dk k2 M2 (k) Pmm(k) ˆP[ m M ]2, m M (k), where we defined the quadratic field  m M 2 (k) ⌘ Z d3 q (2⇡)3 m(q) M(q) m(k q) M(|k q|) M(k, z) ⌘ 2 3 k2 T(k)D(z) ⌦mH2 0 , (83) k, z) (k). Here, T(k) is the linear transfer function normalized to T(k) = 1 on large scales, actor D(z) for ⌦rad = 0 is normalized to D(z) = 1/(1 + z) during matter domination. Note ⌧ keq and M(k) / k0 for k keq. The bispectrum (82) is maximal in the squeezed limit rum (82) into Eq. (36), we get ˆfloc NL = 24⇡L3 Nloc Z dk k2 M2 (k) Pmm(k) ˆP[ m M ]2, m M (k), (84) uadratic field  m M 2 (k) ⌘ Z d3 q (2⇡)3 m(q) M(q) m(k q) M(|k q|) (85) m M (k) ⌘ m(k) M(k) . (86) quals the primordial potential reconstructed from the DM density m. The cross-spectrum the cross-spectrum of this reconstructed with 2 (x), which corresponds to the mechanism al non-Gaussianity of the local kind (adding fNL 2 (x) to (x)). appearing in Eq. (84) could be used to estimate floc NL if the dark matter density was directly ion to observable halo densities is left for future work. It would also be straightforward to a to other separable types of primordial non-Gaussianity generated by other inflation models ogonal). (Poisson factor) ➟ basically just ɸ2 cross ɸ, where ɸ ~ δ/k2! ➟ in principle same S/N as getting from measurement of the entire LSS bispectrum! floc NL floc NL
THEORETICAL 
 MODELING
MODELING CROSS-SPECTRA Expectation value of cross-spectrum with general kernel ! ! ! MS, Baldauf, Seljak, 1411.6595 integrated# bispectrum for D 2 {P0, F1 2 P1, P2} with the den hD[ R a ](k) R b (k0 )i = = For a 6= b, the bispectrum B is not s Writing the smoothing kernels explicit PD[ R a ], R b (k) = WR(k) C For cross-spectra of smoothed dark PD[ R m], R m where we used that the kernel D(q, k hD[ R a ](k) R b (k0 )i = (2⇡)3 D(q, k q)h R a (q) R a (k q) R b (k0 )i = (2⇡)3 D(k + k0 ) Z d3 q (2⇡)3 D(q, k q)B R a R a R b (q, k q, k). = b, the bispectrum B is not symmetric in its arguments and the last argument k is associated w g the smoothing kernels explicitly, we have PD[ R a ], R b (k) = WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)D(q, k q)B a a b (q, k q, k). C. Matter-matter-matter cross-spectra cross-spectra of smoothed dark matter fields (a = b = m), the DM bispectrum (16) gives PD[ R m], R m (k) = Z d3 q (2⇡)3 D(q, k q)B R m R m R m (q, k q, k) = 2IR DF2 (k) + 4Ibare,R DF2 (k), we used that the kernel D(q, k q) is assumed to be symmetric in its arguments and we defined for ker IR DE(k) ⌘ WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)Plin mm(|k q|)D(q, k q)E(q, k q), s symmetric under D $ E, and Ibare,R DE (k) ⌘ WR(k)Plin mm(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)D(q, k q)E(q, k),
MODELING CROSS-SPECTRA Expectation value of cross-spectrum with general kernel ! ! ! MS, Baldauf, Seljak, 1411.6595 integrated# bispectrum for D 2 {P0, F1 2 P1, P2} with the den hD[ R a ](k) R b (k0 )i = = For a 6= b, the bispectrum B is not s Writing the smoothing kernels explicit PD[ R a ], R b (k) = WR(k) C For cross-spectra of smoothed dark PD[ R m], R m where we used that the kernel D(q, k hD[ R a ](k) R b (k0 )i = (2⇡)3 D(q, k q)h R a (q) R a (k q) R b (k0 )i = (2⇡)3 D(k + k0 ) Z d3 q (2⇡)3 D(q, k q)B R a R a R b (q, k q, k). = b, the bispectrum B is not symmetric in its arguments and the last argument k is associated w g the smoothing kernels explicitly, we have PD[ R a ], R b (k) = WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)D(q, k q)B a a b (q, k q, k). C. Matter-matter-matter cross-spectra cross-spectra of smoothed dark matter fields (a = b = m), the DM bispectrum (16) gives PD[ R m], R m (k) = Z d3 q (2⇡)3 D(q, k q)B R m R m R m (q, k q, k) = 2IR DF2 (k) + 4Ibare,R DF2 (k), we used that the kernel D(q, k q) is assumed to be symmetric in its arguments and we defined for ker IR DE(k) ⌘ WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)Plin mm(|k q|)D(q, k q)E(q, k q), s symmetric under D $ E, and Ibare,R DE (k) ⌘ WR(k)Plin mm(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)D(q, k q)E(q, k), 10 for D 2 {P0, F1 2 P1, P2} with the density R b is given by an integral over the bispectrum B, hD[ R a ](k) R b (k0 )i = Z d3 q (2⇡)3 D(q, k q)h R a (q) R a (k q) R b (k0 )i (57) = (2⇡)3 D(k + k0 ) Z d3 q (2⇡)3 D(q, k q)B R a R a R b (q, k q, k). (58) For a 6= b, the bispectrum B is not symmetric in its arguments and the last argument k is associated with b. Writing the smoothing kernels explicitly, we have PD[ R a ], R b (k) = WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)D(q, k q)B a a b (q, k q, k). (59) C. Matter-matter-matter cross-spectra For cross-spectra of smoothed dark matter fields (a = b = m), the DM bispectrum (16) gives PD[ R m], R m (k) = Z d3 q (2⇡)3 D(q, k q)B R m R m R m (q, k q, k) (60) = 2IR DF2 (k) + 4Ibare,R DF2 (k), (61) where we used that the kernel D(q, k q) is assumed to be symmetric in its arguments and we defined for kernels D and E IR DE(k) ⌘ WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)Plin mm(|k q|)D(q, k q)E(q, k q), (62) which is symmetric under D $ E, and Ibare,R DE (k) ⌘ WR(k)Plin mm(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)D(q, k q)E(q, k), (63) which is not symmetric under D $ E. Explicit predictions for the cross-spectra P 2 m, m , P i m@i m, m and Ps2 m, m can be obtained from Eq. (61) by plugging in D = P0, D = F1 2 P1 and D = P2, respectively, and setting E = F2. Note that there are three powers of the smoothing kernel because we smooth the nonlinear rather than the linear field. The integrals in Eqs. (62) and (63) are similar to typical 1-loop expressions and can be reduced to two-dimensional integrals over scale q and cosine ˆq·ˆk, which can be evaluated numerically with little computational cost (see e.g. [43, 44] for public codes that compute similar integrals). The factors WR(|k q|) and Plin mm(|k q|) introduce a non-trivial angle dependence so that the angular integration generally needs to be performed numerically. 10 for D 2 {P0, F1 2 P1, P2} with the density R b is given by an integral over the bispectrum B, hD[ R a ](k) R b (k0 )i = Z d3 q (2⇡)3 D(q, k q)h R a (q) R a (k q) R b (k0 )i (57) = (2⇡)3 D(k + k0 ) Z d3 q (2⇡)3 D(q, k q)B R a R a R b (q, k q, k). (58) For a 6= b, the bispectrum B is not symmetric in its arguments and the last argument k is associated with b. Writing the smoothing kernels explicitly, we have PD[ R a ], R b (k) = WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)D(q, k q)B a a b (q, k q, k). (59) C. Matter-matter-matter cross-spectra For cross-spectra of smoothed dark matter fields (a = b = m), the DM bispectrum (16) gives PD[ R m], R m (k) = Z d3 q (2⇡)3 D(q, k q)B R m R m R m (q, k q, k) (60) = 2IR DF2 (k) + 4Ibare,R DF2 (k), (61) where we used that the kernel D(q, k q) is assumed to be symmetric in its arguments and we defined for kernels D and E IR DE(k) ⌘ WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)Plin mm(|k q|)D(q, k q)E(q, k q), (62) which is symmetric under D $ E, and Ibare,R DE (k) ⌘ WR(k)Plin mm(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)D(q, k q)E(q, k), (63) which is not symmetric under D $ E. Explicit predictions for the cross-spectra P 2 m, m , P i m@i m, m and Ps2 m, m can be obtained from Eq. (61) by plugging in D = P0, D = F1 2 P1 and D = P2, respectively, and setting E = F2. Note that there are three powers of the smoothing kernel because we smooth the nonlinear rather than the linear field. The integrals in Eqs. (62) and (63) are similar to typical 1-loop expressions and can be reduced to two-dimensional integrals over scale q and cosine ˆq·ˆk, which can be evaluated numerically with little computational cost (see e.g. [43, 44] for public codes that compute similar integrals). The factors WR(|k q|) and Plin mm(|k q|) introduce a non-trivial angle dependence so that the angular integration generally needs to be performed numerically. The only ingredient for the theory prediction of Eq. (61) is the model for the DM bispectrum. Improved bispectrum Leading-order SPT for hhh cross-spectra! alo bispectrum (28) gives for the halo-halo-halo cross-spectra 3 1 h IR DF2 (k) + 2Ibare,R DF2 (k) i + 2b2 1b2 h IR DP0 (k) + 2Ibare,R DP0 (k) i + 4 3 b2 1bs2 h IR DP2 (k) + 2Ibare,R DP2 (k) i . (67) ending on b3 1 also appear in matter-matter-halo cross spectra, so that PD[ R h ], R h (k) b2 1PD[ R m], R h (k) = 4b2 1  b2Ibare,R DP0 (k) + 2 3 bs2 Ibare,R DP2 (k) . (68) F2 kernel in Legendre polynomials as in Eq. (18), Eq. (67) can be rewritten as PD[ R h ], R h (k) = ✓ 34 21 b3 1 + 2b2 1b2 ◆ h IR DP0 (k) + 2Ibare,R DP0 (k) i + 2b3 1 h IR D,F 1 2 P1 (k) + 2Ibare,R D,F 1 2 P1 (k) i + ✓ 8 21 b3 1 + 4 3 b2 1bs2 ◆ h IR DP2 (k) + 2Ibare,R DP2 (k) i . (69) ns to the theory expression of Eq. (67) are shown in Fig. 1 for Gaussian smoothing with R = g. 10 in the appendix for R = 10h 1 Mpc). Di↵erent colors describe di↵erent cross-spectra, P2}, while di↵erent line styles correspond to the contributions with di↵erent dependencies on
MODELING CROSS-SPECTRA Expectation value of cross-spectrum with general kernel ! ! ! MS, Baldauf, Seljak, 1411.6595 integrated# bispectrum for D 2 {P0, F1 2 P1, P2} with the den hD[ R a ](k) R b (k0 )i = = For a 6= b, the bispectrum B is not s Writing the smoothing kernels explicit PD[ R a ], R b (k) = WR(k) C For cross-spectra of smoothed dark PD[ R m], R m where we used that the kernel D(q, k hD[ R a ](k) R b (k0 )i = (2⇡)3 D(q, k q)h R a (q) R a (k q) R b (k0 )i = (2⇡)3 D(k + k0 ) Z d3 q (2⇡)3 D(q, k q)B R a R a R b (q, k q, k). = b, the bispectrum B is not symmetric in its arguments and the last argument k is associated w g the smoothing kernels explicitly, we have PD[ R a ], R b (k) = WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)D(q, k q)B a a b (q, k q, k). C. Matter-matter-matter cross-spectra cross-spectra of smoothed dark matter fields (a = b = m), the DM bispectrum (16) gives PD[ R m], R m (k) = Z d3 q (2⇡)3 D(q, k q)B R m R m R m (q, k q, k) = 2IR DF2 (k) + 4Ibare,R DF2 (k), we used that the kernel D(q, k q) is assumed to be symmetric in its arguments and we defined for ker IR DE(k) ⌘ WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)Plin mm(|k q|)D(q, k q)E(q, k q), s symmetric under D $ E, and Ibare,R DE (k) ⌘ WR(k)Plin mm(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)D(q, k q)E(q, k), 13 G. Covariances as and cosmological parameters from cross-spectra we need to know their noise and covariance ng-order perturbation theory predicts for the covariance between two cross-spectra at the same Appendix B) cov( ˆPD[ R a ], R b (k), ˆPE[ R a ], R b (k)) = 2 Nmodes(k) PR bb(k)I P R aa P R aa DE (k), (78) Covariance between cross-spectra at the same k 10 for D 2 {P0, F1 2 P1, P2} with the density R b is given by an integral over the bispectrum B, hD[ R a ](k) R b (k0 )i = Z d3 q (2⇡)3 D(q, k q)h R a (q) R a (k q) R b (k0 )i (57) = (2⇡)3 D(k + k0 ) Z d3 q (2⇡)3 D(q, k q)B R a R a R b (q, k q, k). (58) For a 6= b, the bispectrum B is not symmetric in its arguments and the last argument k is associated with b. Writing the smoothing kernels explicitly, we have PD[ R a ], R b (k) = WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)D(q, k q)B a a b (q, k q, k). (59) C. Matter-matter-matter cross-spectra For cross-spectra of smoothed dark matter fields (a = b = m), the DM bispectrum (16) gives PD[ R m], R m (k) = Z d3 q (2⇡)3 D(q, k q)B R m R m R m (q, k q, k) (60) = 2IR DF2 (k) + 4Ibare,R DF2 (k), (61) where we used that the kernel D(q, k q) is assumed to be symmetric in its arguments and we defined for kernels D and E IR DE(k) ⌘ WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)Plin mm(|k q|)D(q, k q)E(q, k q), (62) which is symmetric under D $ E, and Ibare,R DE (k) ⌘ WR(k)Plin mm(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)D(q, k q)E(q, k), (63) which is not symmetric under D $ E. Explicit predictions for the cross-spectra P 2 m, m , P i m@i m, m and Ps2 m, m can be obtained from Eq. (61) by plugging in D = P0, D = F1 2 P1 and D = P2, respectively, and setting E = F2. Note that there are three powers of the smoothing kernel because we smooth the nonlinear rather than the linear field. The integrals in Eqs. (62) and (63) are similar to typical 1-loop expressions and can be reduced to two-dimensional integrals over scale q and cosine ˆq·ˆk, which can be evaluated numerically with little computational cost (see e.g. [43, 44] for public codes that compute similar integrals). The factors WR(|k q|) and Plin mm(|k q|) introduce a non-trivial angle dependence so that the angular integration generally needs to be performed numerically. 10 for D 2 {P0, F1 2 P1, P2} with the density R b is given by an integral over the bispectrum B, hD[ R a ](k) R b (k0 )i = Z d3 q (2⇡)3 D(q, k q)h R a (q) R a (k q) R b (k0 )i (57) = (2⇡)3 D(k + k0 ) Z d3 q (2⇡)3 D(q, k q)B R a R a R b (q, k q, k). (58) For a 6= b, the bispectrum B is not symmetric in its arguments and the last argument k is associated with b. Writing the smoothing kernels explicitly, we have PD[ R a ], R b (k) = WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)D(q, k q)B a a b (q, k q, k). (59) C. Matter-matter-matter cross-spectra For cross-spectra of smoothed dark matter fields (a = b = m), the DM bispectrum (16) gives PD[ R m], R m (k) = Z d3 q (2⇡)3 D(q, k q)B R m R m R m (q, k q, k) (60) = 2IR DF2 (k) + 4Ibare,R DF2 (k), (61) where we used that the kernel D(q, k q) is assumed to be symmetric in its arguments and we defined for kernels D and E IR DE(k) ⌘ WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)Plin mm(|k q|)D(q, k q)E(q, k q), (62) which is symmetric under D $ E, and Ibare,R DE (k) ⌘ WR(k)Plin mm(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)D(q, k q)E(q, k), (63) which is not symmetric under D $ E. Explicit predictions for the cross-spectra P 2 m, m , P i m@i m, m and Ps2 m, m can be obtained from Eq. (61) by plugging in D = P0, D = F1 2 P1 and D = P2, respectively, and setting E = F2. Note that there are three powers of the smoothing kernel because we smooth the nonlinear rather than the linear field. The integrals in Eqs. (62) and (63) are similar to typical 1-loop expressions and can be reduced to two-dimensional integrals over scale q and cosine ˆq·ˆk, which can be evaluated numerically with little computational cost (see e.g. [43, 44] for public codes that compute similar integrals). The factors WR(|k q|) and Plin mm(|k q|) introduce a non-trivial angle dependence so that the angular integration generally needs to be performed numerically. The only ingredient for the theory prediction of Eq. (61) is the model for the DM bispectrum. Improved bispectrum Leading-order SPT for hhh cross-spectra! alo bispectrum (28) gives for the halo-halo-halo cross-spectra 3 1 h IR DF2 (k) + 2Ibare,R DF2 (k) i + 2b2 1b2 h IR DP0 (k) + 2Ibare,R DP0 (k) i + 4 3 b2 1bs2 h IR DP2 (k) + 2Ibare,R DP2 (k) i . (67) ending on b3 1 also appear in matter-matter-halo cross spectra, so that PD[ R h ], R h (k) b2 1PD[ R m], R h (k) = 4b2 1  b2Ibare,R DP0 (k) + 2 3 bs2 Ibare,R DP2 (k) . (68) F2 kernel in Legendre polynomials as in Eq. (18), Eq. (67) can be rewritten as PD[ R h ], R h (k) = ✓ 34 21 b3 1 + 2b2 1b2 ◆ h IR DP0 (k) + 2Ibare,R DP0 (k) i + 2b3 1 h IR D,F 1 2 P1 (k) + 2Ibare,R D,F 1 2 P1 (k) i + ✓ 8 21 b3 1 + 4 3 b2 1bs2 ◆ h IR DP2 (k) + 2Ibare,R DP2 (k) i . (69) ns to the theory expression of Eq. (67) are shown in Fig. 1 for Gaussian smoothing with R = g. 10 in the appendix for R = 10h 1 Mpc). Di↵erent colors describe di↵erent cross-spectra, P2}, while di↵erent line styles correspond to the contributions with di↵erent dependencies on
SIMULATIONS ! &! RESULTS
SIMULATIONS RunPB sims from Martin White/Beth Reid! L = 1380 Mpc/h# 20483 DM particles or FoF (b=0.168) halos! 10 realizations! z = 0.55 Reid et al. 1404.3742! White et al. 1408.5435! White astro-ph/0207185
DM CROSS-SPECTRA MS, Baldauf, Seljak, 1411.6595 squared density tidal shift
DM CROSS-SPECTRA: COVARIANCE MS, Baldauf, Seljak, 1411.6595 squared density tidalshift Standard deviations of individual cross-spectra (sims divided by theory) Correlations between different cross-spectra at the same k theory (dashed) sims (solid) Perfectly (anti-)correlated at low k and high k, but uncorrelated at intermediate k
HALO CROSS-SPECTRA squared density tidal shift MS, Baldauf, Seljak, 1411.6595 Assuming Poisson shot noise
HALO CROSS-SPECTRA squared density tidal shift MS, Baldauf, Seljak, 1411.6595 Corrected shot noise (rescaling Poisson prediction by free Ashot)
MASS DEPENDENCE OF SHOT NOISE CORRECTION 20 10 12 10 13 10 14 10 15 10 0 10 1 10 2 10 3 10 4 10 5 M [h -1 Msun ] ∆P[h-3 Mpc3 ] z=0 theo z=0.5 theo z=1.0 theo z=0 sim z=0.5 sim z=1.0 sim of the stochasticity correction on halo mass and redshift. The lines are based on linear bias parameters ound split and a fitting function modelling b2. The dashed lines and open points describe negative values. ow-k limits of the one and two bin splitting as the diamond and squares, respectively. The dotted line w mass correction arising from non-linear biasing whereas the dash-dotted blue, green and red lines show ss exclusion corrections for the three di↵erent redshifts. 1 Correction to 1/n shot noise in P(k) (Baldauf et al. 1305.2917) Correction to P(k)/n part of shot noise in bispectrum
BIAS CONSTRAINTS 21 h 2 h| hi h i h i h| hi hs2 h| hi combined 0.5 0.0 0.5 1.0 1.5 b2 2 1 0 1 2 3 bs2 1.2 1.4 1.6 1.8 2.0 2.2 2.4 b1 0.4 0.2 0.0 0.2 0.4 As 0.5 0.0 0.5 1.0 1.5 b2 2 1 0 1 2 3 bs2 MS, Baldauf, Seljak, 1411.6595 squared density tidal shift Fit b1, b2, bs2, Ashot from hhh cross-spectra (only 3-point, no 2-point)
BIAS CONSTRAINTS 21 h 2 h| hi h i h i h| hi hs2 h| hi combined 0.5 0.0 0.5 1.0 1.5 b2 2 1 0 1 2 3 bs2 1.2 1.4 1.6 1.8 2.0 2.2 2.4 b1 0.4 0.2 0.0 0.2 0.4 As 0.5 0.0 0.5 1.0 1.5 b2 2 1 0 1 2 3 bs2 MS, Baldauf, Seljak, 1411.6595 squared density tidal shift 
 l=1 shift contains almost entire b1 information Fit b1, b2, bs2, Ashot from hhh cross-spectra (only 3-point, no 2-point)
CONCLUDING! REMARKS
 (PART I)
COMPARISON WITH TRADITIONAL BISPECTRUM ESTIMATION Advantages of cross-spectra over estimating bispectrum for individual triangles! Simple# Nearly optimal (contains entire bispectrum information)! Fast (like power spectrum estimation)! Depends on just single k, i.e. covariances are easy! Challenges! Redshift space distortions: messier but doable (ideas/tricks welcome!)! Not optimal beyond tree level and beyond weakly non-Gaussian fields (could be improved with better theory or by combining with modal estimators that are sensitive to arbitrary bispectra) MS, Baldauf, Seljak, 1411.6595 Position-dependent power spectrum (Chiang++) is only sensitive to squeezed limit where grav. signal vanishes
WHAT DO CMB PEOPLE DO? CMB bispectrum estimation! 2001: individual triangles of COBE data (lmax=20)! since 2003: KSW fNL estimators for separable bispectrum templates (now standard)! CMB lensing trispectrum:! ➟ never used individual quadrilaterals, but always max- likeli estimators for known trispectrum shape! ➟ reconstructed lensing potential is ! ➟ trispectrum = auto-power of this quadratic field! ISW-lensing bispectrum: cross-spectrum of and T! modal estimators (sensitive to arbitrary bispectra — reduce to our cross-spectra if halo bispectrum contributions are used as basis shapes) cr / TrT ˆ ⇠ T2 Komatsu et al. 2001 Komatsu+Spergel +Wandelt 2003! Lewis et al. 2011,
 Planck 2013 XIX (ISW) Fergusson+Shellard +Liguori 2009-2014,! Planck 2013 XXIV (NG)
WHAT DO CMB PEOPLE DO? CMB bispectrum estimation! 2001: individual triangles of COBE data (lmax=20)! since 2003: KSW fNL estimators for separable bispectrum templates (now standard)! CMB lensing trispectrum:! ➟ never used individual quadrilaterals, but always max- likeli estimators for known trispectrum shape! ➟ reconstructed lensing potential is ! ➟ trispectrum = auto-power of this quadratic field! ISW-lensing bispectrum: cross-spectrum of and T! modal estimators (sensitive to arbitrary bispectra — reduce to our cross-spectra if halo bispectrum contributions are used as basis shapes) cr / TrT ˆ ⇠ T2 Komatsu et al. 2001 Komatsu+Spergel +Wandelt 2003! Lewis et al. 2011,
 Planck 2013 XIX (ISW) Fergusson+Shellard +Liguori 2009-2014,! Planck 2013 XXIV (NG) 
 LSS methods at the level where CMB was 2001
WHAT DO CMB PEOPLE DO? CMB bispectrum estimation! 2001: individual triangles of COBE data (lmax=20)! since 2003: KSW fNL estimators for separable bispectrum templates (now standard)! CMB lensing trispectrum:! ➟ never used individual quadrilaterals, but always max- likeli estimators for known trispectrum shape! ➟ reconstructed lensing potential is ! ➟ trispectrum = auto-power of this quadratic field! ISW-lensing bispectrum: cross-spectrum of and T! modal estimators (sensitive to arbitrary bispectra — reduce to our cross-spectra if halo bispectrum contributions are used as basis shapes) cr / TrT ˆ ⇠ T2 Komatsu et al. 2001 Komatsu+Spergel +Wandelt 2003! Lewis et al. 2011,
 Planck 2013 XIX (ISW) Fergusson+Shellard +Liguori 2009-2014,! Planck 2013 XXIV (NG) 
 LSS methods at the level where CMB was 2001 
 Cross-spectrum method is first step to modernize this for LSS! 
 but it’s really only a first step: 
 our method is not yet ready for real data, 
 while traditional method has been applied to real data (most recently by Gil-Marin et al. 2014)
Explored new estimators to measure bispectrum parameters in a nearly optimal way using cross-spectra of 3 quadratic fields with the density! ! ! ! ! Simulations agree with theory on large scales! Linear halo bias b1 mostly determined by shift term cross density! Shot noise requires non-Poissonian corrections ! Future: e.g. RSDs, model to higher k, apply to real data CONCLUSIONS (PART I) MS, Baldauf, Seljak, 1411.6595 te the density gradient @i (x), the displacement field i(x) = @i@ 2 (x), and the tidal tensor sij(x 2 1 3 (K) ij ⇤ (x). te the squared density 2 (x), the shift term i (x)@i (x) and the tidal term s2 (x) = 3 2 sij(x)sij(x). transform the three quadratic fields to get [ 2 ](k), [ i @i ](k) and [s2 ](k). te the cross-spectra between the quadratic fields and the density, i.e. (suppressing division by the num es) ˆP 2, (k) ⇠ X k,|k|=k [ 2 ](k) ( k), ( ˆP i@i , (k) ⇠ X k,|k|=k [ i @i ](k) ( k) ( ˆPs2, (k) ⇠ X k,|k|=k [s2 ](k) ( k). ( ed from the Legendre decomposition of the halo bispectrum, the l = 1 shift cross-spectrum (88) conta ntire bispectrum information on the linear bias b1, while the l = 0 squared density cross-spectrum ( 2 tidal cross-spectrum (89) mostly improve constraints on b2 and bs2 once b1 is known. Measuring pectra and comparing them to their theory predictions is equivalent to an optimal maximum-likelih the amplitudes of contributions to dark matter or halo bispectra (under certain regularity conditions; Therefore, these cross-spectra contain the same constraining power on bias parameters and 8 as a ectrum analysis. Measuring cross-spectra is both simpler and computationally cheaper than perform trum measurements for individual triangle configurations. Since they only depend on a single rather t umbers, modeling the covariance is also simpler. derived leading-order perturbation theory predictions for the expectation values and covariances of pectra, where both the quadratic and the single field can be dark matter or halo fields, and sec
PART II! 
 EULERIAN BAO RECONSTRUCTIONS 
 & 
 N-POINT STATISTICS! 
 
 ARXIV:1508.06972! W/ Y. FENG, F. BEUTLER, B. SHERWIN, M.Y. CHU
BARYONIC ACOUSTIC OSCILLATIONS (BAO) Eisenstein/Seo/White 2006! – 5 – Coupled baryon- photon fluid
BARYONIC ACOUSTIC OSCILLATIONS (BAO) Eisenstein/Seo/White 2006! – 5 – Coupled baryon- photon fluid – 5 – Expansion
BARYONIC ACOUSTIC OSCILLATIONS (BAO) Eisenstein/Seo/White 2006! – 5 – Coupled baryon- photon fluid – 5 – Expansion – 5 – Decoupling Photons free-stream
BARYONIC ACOUSTIC OSCILLATIONS (BAO) Eisenstein/Seo/White 2006! – 5 – Coupled baryon- photon fluid – 5 – Expansion – 5 – Decoupling Photons free-stream – 5 – Baryons cluster in shell Expansion BAO scale r=150Mpc
Eisenstein/Seo/White 2006! s profile versus comoving radius of an initially point-like BAO scale r=150Mpc The As the perturbation g equilibrium The final configuration put in by hand) and an “ Further (non-linear) proce slightly shift the peak -- b Expansion + clustering BARYONIC ACOUSTIC OSCILLATIONS (BAO)
BAO IMPRINT ON LSS Correlation function : BAO bump Power spectrum P(k): BAO wiggles⇠(r) x 0.7!
BAO IMPRINT ON LSS Non-linearities: ! Add small-scale power! Smear out BAO peak, i.e. degrade BAO information! Slightly shift BAO scale! Correlation function : BAO bump Power spectrum P(k): BAO wiggles⇠(r) x 0.7!
BAO RECONSTRUCTION Goal: Restore linear modes to increase BAO information! ! Method: Undo large-scale flows that broaden the BAO peak:! Calculate large-scale displacement/velocity field with Zeldovich approximation! ! ! Displace clustered and random catalog by this! ➡ get `displaced’ and `shifted’ densities δd and δs! Reconstructed density = δd - δs s(k) = ik k2 WR(k) (k) power spectrum after reconstruction by Fig. 3.— The real-space matter correlation function after recon struction by the linear-theory density-velocity relation, with th density field Gaussian filtered. The black solid line shows the co relation function at z = 49. The blue short-dashed line shows at z = 0.3; the acoustic peak has been smeared out. The re dot-dashed and magenta long-dashed lines show the effects of r construction for 20h−1 Mpc and 10h−1 Mpc Gaussian filtering, r no reconstruction linear reconstructed Simulations Eisenstein et al. 2006! Padmanabhan et al. 2008
BAO RECONSTRUCTION Works great in practice: 
 For BOSS DR11, BAO signal-to-noise improved by ~50%, 
 achieving sub-percent BAO scale constraint Padmanabhan et al. 2012! Anderson et al. 2013! BAO in SDSS-III BOSS galaxies 17 Figure 11. DR11 CMASS clustering measurements (black circles) with ⇠(s) shown in the left panels and P(k) in the right panels. The top panels show the Anderson et al. 2013! (BOSS DR11)!
NEW ! RECONSTRUCTION! ALGORITHMS
NEW ALGORITHMS: WHY? Motivations to dig deeper & explore new reconstruction algorithms:! Where does the new BAO information come from?! Can we derive reconstruction algorithm(s) better?! Can we do better in terms of BAO information?! Can we reduce intermixing of model and data when reconstructing?! (e.g. standard reconstruction makes assumptions on f, galaxy bias, gravity when transforming data)! ! Tried to address some of these points in recent paper (1508.06972)
NEW ALGORITHMS: CATEGORIES Useful to consider two categories of reconstruction algorithms: Eulerian reconstructions Never displace any objects! Work only with Eulerian density! E.g. rec = 2
NEW ALGORITHMS: CATEGORIES Useful to consider two categories of reconstruction algorithms: Lagrangian reconstructions Displace objects at some stage of the algorithm! E.g. standard algorithm (Eisenstein et al. 2006) Eulerian reconstructions Never displace any objects! Work only with Eulerian density! E.g. rec = 2
NEW EULERIAN ALGORITHM I Eulerian growth-shift (EGS) reconstruction! Go back in time MS et al. 1508.06972! rec(x) = (x, ⌘ ⌘) ⇡ (x, ⌘) ⌘ @⌘ (x, ⌘)
NEW EULERIAN ALGORITHM I Eulerian growth-shift (EGS) reconstruction! Go back in time MS et al. 1508.06972! rec(x) = (x, ⌘ ⌘) ⇡ (x, ⌘) ⌘ @⌘ (x, ⌘) @⌘ + r · [(1 + )v] = 0 ) @⌘ = r · v v · r r · v Get density time derivative nonperturbatively from nonlinear continuity equation
NEW EULERIAN ALGORITHM I Eulerian growth-shift (EGS) reconstruction! Go back in time MS et al. 1508.06972! Approximate velocity in terms of smoothed density δR (using linear relation)! ) r · v = Rv(k) ⇡ s(k) ⌘ ik k2 R(k) - Not reverting linear time evolution! - Not displacing any objects, i.e. Eulerian! - Nonperturbative in δ, linear in velocity ) rec EGS(x) = (x) s(x) · r (x) | {z } shift (x) R(x) | {z } growth rec(x) = (x, ⌘ ⌘) ⇡ (x, ⌘) ⌘ @⌘ (x, ⌘) @⌘ + r · [(1 + )v] = 0 ) @⌘ = r · v v · r r · v Get density time derivative nonperturbatively from nonlinear continuity equation
NEW EULERIAN ALGORITHM I Eulerian growth-shift (EGS) reconstruction! Reconstructed power spectrum:! MS et al. 1508.06972! 2 ⌦ 2 | ↵ 2 hs · r | i + ⌦ 2 | 2 ↵ + hs · r |s · r i + 2 ⌦ 2 |s · r ↵ 2-point 3-point 4-point ) h rec EGS| rec EGSi = h | i ➡ Automatically combines 2-, 3- and 4-point of the unreconstructed density! ➡ Explicitly see how reconstruction exploits 3- and 4-point BAO information! ➡ Cross-spectra are the same as in part I of the talk, i.e. nearly-optimal! ➡ Get similar combinations for all our other Eulerian reconstructions rec EGS = 2 s · r ) h rec EGS| rec EGSi = h | i
NEW EULERIAN ALGORITHM II Eulerian F2 (EF2) reconstruction! Second-order density! ! ! Remove this from the full density MS et al. 1508.06972! (2) (x) = 17 21 2 0(x) | {z } growth 0(x) · r 0(x) | {z } shift + 4 21 K2 0 (x) | {z } tidal rec EF2(x) ⌘ (x) 17 21 R(x) (x) | {z } growth s(x) · r (x) | {z } shift 4 21 K2 R(x) | {z } tidal More formal derivation using Newton-Raphson iteration: 
 Find linear density compatible with a given observed nonlinear density, i.e. solve
 for δ0!f[ 0] ⌘ 0 + F2[ 0] obs = 0
NEW LAGRANGIAN ALGORITHMS Lagrangian Random-random (LRR) reconstruction! 2LPT theory for clustered and random catalogs displaced by negative or positive Zeldovich displacement: δd[s], δd[-s], δs[s], δs[-s] ! Only 2 of 6 possible combinations suppress 2nd order nonlinearities:! Lagrangian growth-shift (LGS) reconstruction (= standard algorithm)! ! Lagrangian random-random (LRR) reconstruction:! ! ! MS et al. 1508.06972! rec LGS = d[s] s[s] rec LRR = 1 2 ⇢ s[s] + s[ s]
NEW LAGRANGIAN ALGORITHMS Lagrangian F2 (LF2) reconstruction! Unique combination of LRR and standard LGS algorithms reverses full second-order density (up to smoothing)! MS et al. 1508.06972! rec LF2 = 3 7 rec LGS + 4 7 rec LRR
OVERVIEW All reconstruction algorithms MS et al. 1508.06972! Eulerian Lagrangian Growth-Shift rec EGS(x) = (x) R(x) (x) s(x) · r (x) rec LGS = d[s] Full F2 rec EF2(x) = (x) 17 21 R(x) (x) s(x)r (x) 4 21 K2 R(x) rec LF2 = 3 7 rec LGS Random-Random rec ERR = (x) 2 3 2 R(x) s(x) · r R(x) 1 3 K2 RR(x) rec LRR = 1 2 TABLE I. Overview of the reconstruction algorithms considered in this paper. Lagrangian reconstructions involv of objects which is not the case for Eulerian reconstructions. Methods in the same row agree at second order LPT BAO reconstruction algorithm is called Lagrangian growth-shift reconstruction and listed on the upper right. 1. LGS: Lagrangian Growth-Shift Reconstruction (Section IV B) This is the standard BAO reconstruction algorithm proposed in [8]: Objects in clustered and rando displaced by the large-scale negative Zeldovich displacement field s, and the di↵erence between the ‘displaced’ and ‘shifted ’densities d[s] and s[s] gives the reconstructed density, rec LGS = d[s] s[s]. Eulerian Lagrangian wth-Shift rec EGS(x) = (x) R(x) (x) s(x) · r (x) rec LGS = d[s] s[s] F2 rec EF2(x) = (x) 17 21 R(x) (x) s(x)r (x) 4 21 K2 R(x) rec LF2 = 3 7 rec LGS + 4 7 rec LRR dom-Random rec ERR = (x) 2 3 2 R(x) s(x) · r R(x) 1 3 K2 RR(x) rec LRR = 1 2 { s[s] + s LE I. Overview of the reconstruction algorithms considered in this paper. Lagrangian reconstructions involve displace jects which is not the case for Eulerian reconstructions. Methods in the same row agree at second order LPT. The sta O reconstruction algorithm is called Lagrangian growth-shift reconstruction and listed on the upper right. . LGS: Lagrangian Growth-Shift Reconstruction (Section IV B) This is the standard BAO reconstruction algorithm proposed in [8]: Objects in clustered and random catalog displaced by the large-scale negative Zeldovich displacement field s, and the di↵erence between the correspon ‘displaced’ and ‘shifted ’densities d[s] and s[s] gives the reconstructed density, rec LGS = d[s] s[s]. 11 Eulerian Lagrangian rec EGS(x) = (x) R(x) (x) s(x) · r (x) rec LGS = d[s] s[s] rec EF2(x) = (x) 17 21 R(x) (x) s(x)r (x) 4 21 K2 R(x) rec LF2 = 3 7 rec LGS + 4 7 rec LRR om rec ERR = (x) 2 3 2 R(x) s(x) · r R(x) 1 3 K2 RR(x) rec LRR = 1 2 { s[s] + s[ s]} rview of the reconstruction algorithms considered in this paper. Lagrangian reconstructions involve displacements h is not the case for Eulerian reconstructions. Methods in the same row agree at second order LPT. The standard uction algorithm is called Lagrangian growth-shift reconstruction and listed on the upper right. agrangian Growth-Shift Reconstruction (Section IV B) he standard BAO reconstruction algorithm proposed in [8]: Objects in clustered and random catalogs are d by the large-scale negative Zeldovich displacement field s, and the di↵erence between the corresponding ed’ and ‘shifted ’densities d[s] and s[s] gives the reconstructed density, rec LGS = d[s] s[s]. (57) Eulerian Lagrangian Equivalent in 2LPT* * If displaced clustered catalogs are modelled such that displacement field is evaluated at Eulerian, not Lagrangian position, i.e. s[x] instead of s[q].
SIMULATIONS
SIMULATIONS FastPM code by Yu Feng! Particle-mesh code, time-stepping linear in scale factor a, parallel COLA! Ran wiggle and nowiggle simulations with same initial phases! 20483 DM particles, L=1380Mpc/h, 80 timesteps, 3 realizations, take 1% subsample at z=0.55; use R=15Mpc/h smoothing # ! RunPB simulations by Martin White! Full TreePM (more accurate and expensive)! Only wiggle simulations!
OVERVIEW All reconstruction algorithms MS et al. 1508.06972! Eulerian Lagrangian Growth-Shift rec EGS(x) = (x) R(x) (x) s(x) · r (x) rec LGS = d[s] Full F2 rec EF2(x) = (x) 17 21 R(x) (x) s(x)r (x) 4 21 K2 R(x) rec LF2 = 3 7 rec LGS Random-Random rec ERR = (x) 2 3 2 R(x) s(x) · r R(x) 1 3 K2 RR(x) rec LRR = 1 2 TABLE I. Overview of the reconstruction algorithms considered in this paper. Lagrangian reconstructions involv of objects which is not the case for Eulerian reconstructions. Methods in the same row agree at second order LPT BAO reconstruction algorithm is called Lagrangian growth-shift reconstruction and listed on the upper right. 1. LGS: Lagrangian Growth-Shift Reconstruction (Section IV B) This is the standard BAO reconstruction algorithm proposed in [8]: Objects in clustered and rando displaced by the large-scale negative Zeldovich displacement field s, and the di↵erence between the ‘displaced’ and ‘shifted ’densities d[s] and s[s] gives the reconstructed density, rec LGS = d[s] s[s]. Eulerian Lagrangian wth-Shift rec EGS(x) = (x) R(x) (x) s(x) · r (x) rec LGS = d[s] s[s] F2 rec EF2(x) = (x) 17 21 R(x) (x) s(x)r (x) 4 21 K2 R(x) rec LF2 = 3 7 rec LGS + 4 7 rec LRR dom-Random rec ERR = (x) 2 3 2 R(x) s(x) · r R(x) 1 3 K2 RR(x) rec LRR = 1 2 { s[s] + s LE I. Overview of the reconstruction algorithms considered in this paper. Lagrangian reconstructions involve displace jects which is not the case for Eulerian reconstructions. Methods in the same row agree at second order LPT. The sta O reconstruction algorithm is called Lagrangian growth-shift reconstruction and listed on the upper right. . LGS: Lagrangian Growth-Shift Reconstruction (Section IV B) This is the standard BAO reconstruction algorithm proposed in [8]: Objects in clustered and random catalog displaced by the large-scale negative Zeldovich displacement field s, and the di↵erence between the correspon ‘displaced’ and ‘shifted ’densities d[s] and s[s] gives the reconstructed density, rec LGS = d[s] s[s]. 11 Eulerian Lagrangian rec EGS(x) = (x) R(x) (x) s(x) · r (x) rec LGS = d[s] s[s] rec EF2(x) = (x) 17 21 R(x) (x) s(x)r (x) 4 21 K2 R(x) rec LF2 = 3 7 rec LGS + 4 7 rec LRR om rec ERR = (x) 2 3 2 R(x) s(x) · r R(x) 1 3 K2 RR(x) rec LRR = 1 2 { s[s] + s[ s]} rview of the reconstruction algorithms considered in this paper. Lagrangian reconstructions involve displacements h is not the case for Eulerian reconstructions. Methods in the same row agree at second order LPT. The standard uction algorithm is called Lagrangian growth-shift reconstruction and listed on the upper right. agrangian Growth-Shift Reconstruction (Section IV B) he standard BAO reconstruction algorithm proposed in [8]: Objects in clustered and random catalogs are d by the large-scale negative Zeldovich displacement field s, and the di↵erence between the corresponding ed’ and ‘shifted ’densities d[s] and s[s] gives the reconstructed density, rec LGS = d[s] s[s]. (57) Eulerian Lagrangian Equivalent in 2LPT* * If displaced clustered catalogs are modelled such that displacement field is evaluated at Eulerian, not Lagrangian position, i.e. s[x] instead of s[q].
OVERVIEW All reconstruction algorithms MS et al. 1508.06972! Eulerian Lagrangian Growth-Shift rec EGS(x) = (x) R(x) (x) s(x) · r (x) rec LGS = d[s] Full F2 rec EF2(x) = (x) 17 21 R(x) (x) s(x)r (x) 4 21 K2 R(x) rec LF2 = 3 7 rec LGS Random-Random rec ERR = (x) 2 3 2 R(x) s(x) · r R(x) 1 3 K2 RR(x) rec LRR = 1 2 TABLE I. Overview of the reconstruction algorithms considered in this paper. Lagrangian reconstructions involv of objects which is not the case for Eulerian reconstructions. Methods in the same row agree at second order LPT BAO reconstruction algorithm is called Lagrangian growth-shift reconstruction and listed on the upper right. 1. LGS: Lagrangian Growth-Shift Reconstruction (Section IV B) This is the standard BAO reconstruction algorithm proposed in [8]: Objects in clustered and rando displaced by the large-scale negative Zeldovich displacement field s, and the di↵erence between the ‘displaced’ and ‘shifted ’densities d[s] and s[s] gives the reconstructed density, rec LGS = d[s] s[s]. Eulerian Lagrangian wth-Shift rec EGS(x) = (x) R(x) (x) s(x) · r (x) rec LGS = d[s] s[s] F2 rec EF2(x) = (x) 17 21 R(x) (x) s(x)r (x) 4 21 K2 R(x) rec LF2 = 3 7 rec LGS + 4 7 rec LRR dom-Random rec ERR = (x) 2 3 2 R(x) s(x) · r R(x) 1 3 K2 RR(x) rec LRR = 1 2 { s[s] + s LE I. Overview of the reconstruction algorithms considered in this paper. Lagrangian reconstructions involve displace jects which is not the case for Eulerian reconstructions. Methods in the same row agree at second order LPT. The sta O reconstruction algorithm is called Lagrangian growth-shift reconstruction and listed on the upper right. . LGS: Lagrangian Growth-Shift Reconstruction (Section IV B) This is the standard BAO reconstruction algorithm proposed in [8]: Objects in clustered and random catalog displaced by the large-scale negative Zeldovich displacement field s, and the di↵erence between the correspon ‘displaced’ and ‘shifted ’densities d[s] and s[s] gives the reconstructed density, rec LGS = d[s] s[s]. 11 Eulerian Lagrangian rec EGS(x) = (x) R(x) (x) s(x) · r (x) rec LGS = d[s] s[s] rec EF2(x) = (x) 17 21 R(x) (x) s(x)r (x) 4 21 K2 R(x) rec LF2 = 3 7 rec LGS + 4 7 rec LRR om rec ERR = (x) 2 3 2 R(x) s(x) · r R(x) 1 3 K2 RR(x) rec LRR = 1 2 { s[s] + s[ s]} rview of the reconstruction algorithms considered in this paper. Lagrangian reconstructions involve displacements h is not the case for Eulerian reconstructions. Methods in the same row agree at second order LPT. The standard uction algorithm is called Lagrangian growth-shift reconstruction and listed on the upper right. agrangian Growth-Shift Reconstruction (Section IV B) he standard BAO reconstruction algorithm proposed in [8]: Objects in clustered and random catalogs are d by the large-scale negative Zeldovich displacement field s, and the di↵erence between the corresponding ed’ and ‘shifted ’densities d[s] and s[s] gives the reconstructed density, rec LGS = d[s] s[s]. (57) Eulerian Lagrangian Equivalent in 2LPT* * If displaced clustered catalogs are modelled such that displacement field is evaluated at Eulerian, not Lagrangian position, i.e. s[x] instead of s[q].
NUMERICAL RESULTS:! GROWTH-SHIFT! ALGORITHMS
GROWTH-SHIFT ALGORITHMS 2D slice plots
 Density change due to reconstruction, δ - δrec MS et al. 1508.06972! Eulerian growth-shift (EGS) Lagrangian growth-shift (LGS)
GROWTH-SHIFT ALGORITHMS BAO signal: Fractional difference of power spectra between wiggle and nowiggle simulations, for Lagrangian and Eulerian growth-shift algorithms! MS et al. 1508.06972! No reconstruction Perfect reconstruction Eulerian growth-shift Lagrangian growth-shift
GROWTH-SHIFT ALGORITHMS Cumulative BAO signal-to-noise-squared:
 Eulerian algorithm yields 95% of BAO S/N of standard Lagrangian algorithm MS et al. 1508.06972! No reconstruction Perfect reconstruction Eulerian growth-shift Lagrangian growth-shift
WHERE DOES! NEW BAO INFO! COME FROM?
GROWTH-SHIFT ALGORITHMS 3-point vs 4-point: Split δrec = δ + (δrec-δ) in simulations MS et al. 1508.06972! ) h rec | rec i = h | i |{z} 2pt + 2 h( rec )| i | {z } 3pt + h( rec )|( rec )i | {z } 4pt .
GROWTH-SHIFT ALGORITHMS 3-point vs 4-point: Split δrec = δ + (δrec-δ) in simulations MS et al. 1508.06972! ) h rec | rec i = h | i |{z} 2pt + 2 h( rec )| i | {z } 3pt + h( rec )|( rec )i | {z } 4pt . ➡ 3-point much more important than 4-point
GROWTH-SHIFT ALGORITHMS 13 vs 22 part (of 3-point): Split δ = δ0 + (δ-δ0) in simulations, where δ0 = linear density MS et al. 1508.06972! ) h( rec )| i = h( rec )| 0i | {z } ⇠h (3) 0i + h( rec )|( 0)i | {z } ⇠h (2) (2) i
GROWTH-SHIFT ALGORITHMS 13 vs 22 part (of 3-point): Split δ = δ0 + (δ-δ0) in simulations, where δ0 = linear density MS et al. 1508.06972! ➡ 13 part of 3-point sharpens BAO peaks,
 22-part shifts and damps them 
 (see also Padmanabhan et al 2009) ) h( rec )| i = h( rec )| 0i | {z } ⇠h (3) 0i + h( rec )|( 0)i | {z } ⇠h (2) (2) i
GROWTH-SHIFT ALGORITHMS Growth vs shift part MS et al. 1508.06972!
GROWTH-SHIFT ALGORITHMS Growth vs shift part MS et al. 1508.06972! ➡ Shift part most important, 
 but growth also crucial
NUMERICAL RESULTS:! ALL! ALGORITHMS
ALL ALGORITHMS Performance comparison! EGS algorithm yields 95% of BAO S/N of standard Lagrangian LGS algorithm! EF2 algorithm similar, other algorithms significantly worse MS et al. 1508.06972!
DISCUSSION Pros & Cons MS et al. 1508.06972! Lagrangian reconstructions - Less transparent where BAO info comes from! - Data transformation very dependent on fiducial model! - Justification of algorithm mostly a posteriori
 ! +Slightly better performance! +Well established (10 yr old), applied to observations Eulerian reconstructions +BAO info comes from specific 3- & 4-point! +Data transformations less dependent on fiducial model! +Derived from nonperturbative continuity equation (EGS) or Newton-Raphson (EF2)! - Slightly worse performance! - Not worked out in redshift space
DISCUSSION Pros & Cons MS et al. 1508.06972! Lagrangian reconstructions - Less transparent where BAO info comes from! - Data transformation very dependent on fiducial model! - Justification of algorithm mostly a posteriori
 ! +Slightly better performance! +Well established (10 yr old), applied to observations Eulerian reconstructions +BAO info comes from specific 3- & 4-point! +Data transformations less dependent on fiducial model! +Derived from nonperturbative continuity equation (EGS) or Newton-Raphson (EF2)! - Slightly worse performance! - Not worked out in redshift space Equivalent in 2LPT! ! ➡ Reconstructions are connected! ➡ New argument for success and robustness of standard reconstruction: implicitly includes 3- and 4-point! ➡ Intuitively, expect little new BAO information in 3-point statistics, because rec. moves it into 2-point
CONCLUSIONS!
CONCLUSIONS Simple bispectrum estimators! Three cross-spectra of quadratic fields with the density measure projection of full bispectrum on tree-level shape! Simple, fast, nearly-optimal, simpler covariances! Works well in simulations! Extension to redshift space under development! ! Eulerian BAO reconstruction and higher N-point statistics! Presented 5 new BAO reconstruction algorithms! Showed connection to 3- & 4-point of unreconstructed density! Connected various algorithms to each other via 2LPT modelling! Standard algorithm performs best, but two Eulerian algorithms are almost as well, achieving ~95% BAO S/N of standard method! MS et al. 1508.06972! MS, Baldauf, Seljak 1411.6595!
Thank you
BISPECTRUM! BONUS! SLIDES!
11 FIG. 1. Theory contributions (67) to halo-halo-halo cross-spectra scaling like b3 1 (dashed), b2 1b2 (dash-dotted) and b2 1bs2 (dotted) for squared density 2 h(x) (blue), shift term i h(x)@i h(x) (red) and tidal term s2 h(x) (green), evaluated for fixed bias parameters b1 = 1, b2 = 0.5 and bs2 = 2, Gaussian smoothing with RG = 20h 1 Mpc, at z = 0.55, with linear matter power spectra in integrands. Thin gray lines show the large-scale (low k) limit given by Eq. (70). The cross-spectra are divided by the partially smoothed FrankenEmu emulator matter power spectrum W 3/2 R Pemu mm [45–48] for plotting convenience. THEORY CONTRIBUTIONS
VERY LARGE SCALES Large scale limit (k→0) of cross-spectrum expectation values: MS, Baldauf, Seljak, 1411.6595 0 IR DE(k) = WR(k)⌧4 R, D 2 P0, F1 2 P1, P2 , E 2 P0, P2 , (A15) ⌧4 R ⌘ 1 2⇡2 Z dq q2 W2 R(q)P2 mm(q). (A16) tegrals (63) for E = F2 follow from Eqs. (A10) and (A12) by noting that only P2 0(⌫) tegration over the product of Eqs. (A10) and (A12) because of Legendre polynomial G (k) = 1 4 WR(k)Pmm(k) ✓ 68 21 2 R 2 3 2 R,W 0 R ◆ , D 2 P0, F1 2 P1, P2 , (A17) 2 R ⌘ 1 2⇡2 Z dq q2 W2 R(q)Pmm(q) (A18) 2 R,W 0 R ⌘ 1 2⇡2 Z dq q2 d ln WR(q) d ln q W2 R(q)Pmm(q). (A19) d ln WR(q)/d ln q = (qR)2 , so no numerical derivatives are required to evaluate Eq. (A19). can be integrated by parts to get 2 R,W 0 R = 1 2 2 R,P 0 , where 2 R,P 0 ⌘ 1 2⇡2 Z dq q2 W2 R(q)Pmm(q) d ln q3 Pmm(q) d ln q . (A20) r D 2 {P0, F1 2 P1, P2} k) = 1 4 WR(k)Pmm(k) ✓ 68 21 2 R 1 3 2 R,P 0 ◆ (A21) = 1 4 WR(k)Pmm(k) 1 2⇡2 Z dq q2 W2 R(q)Pmm(q) ✓ 68 21 1 3 d ln q3 Pmm(q) d ln q ◆ . (A22) ⌧4 R ⌘ 1 2⇡2 dq q2 W2 R(q)P2 mm(q). (A16) e bare integrals (63) for E = F2 follow from Eqs. (A10) and (A12) by noting that only P2 0(⌫) the ⌫ integration over the product of Eqs. (A10) and (A12) because of Legendre polynomial m !0 Ibare,RG DF2 (k) = 1 4 WR(k)Pmm(k) ✓ 68 21 2 R 2 3 2 R,W 0 R ◆ , D 2 P0, F1 2 P1, P2 , (A17) 2 R ⌘ 1 2⇡2 Z dq q2 W2 R(q)Pmm(q) (A18) 2 R,W 0 R ⌘ 1 2⇡2 Z dq q2 d ln WR(q) d ln q W2 R(q)Pmm(q). (A19) thing, d ln WR(q)/d ln q = (qR)2 , so no numerical derivatives are required to evaluate Eq. (A19). Eq. (A19) can be integrated by parts to get 2 R,W 0 R = 1 2 2 R,P 0 , where 2 R,P 0 ⌘ 1 2⇡2 Z dq q2 W2 R(q)Pmm(q) d ln q3 Pmm(q) d ln q . (A20) ecomes for D 2 {P0, F1 2 P1, P2} Ibare,RG DF2 (k) = 1 4 WR(k)Pmm(k) ✓ 68 21 2 R 1 3 2 R,P 0 ◆ (A21) = 1 4 WR(k)Pmm(k) 1 2⇡2 Z dq q2 W2 R(q)Pmm(q) ✓ 68 21 1 3 d ln q3 Pmm(q) d ln q ◆ . (A22) tains the linear response of the power spectrum to a long-wavelength overdensity (e.g. [17, 33, 58]). rge-scale limit of the quantity proposed in [17] is (by construction) proportional to the response cales rather than integrating over it. y the P0(⌫) part of Eq. (A12) survives the ⌫-integration so that lim k!0 Ibare,R DP0 (k) = WR(k)Pmm(k) 2 R, D 2 P0, F1 2 P1, P2 . (A23) 24 WR(|k q|) = WR(q)  1 k⌫ q d ln WR(q) d ln q . (A12) s becomes WRG (|k q|) = WRG (q)  1 + k⌫ q (qR)2 . (A13) der (k/q)0 for the final results from now on we get from Eqs. (A7), (A8), (A11) and (A12) lim k!0 IR DF2 (k) = 0, D 2 P0, F1 2 P1, P2 . (A14) m 0 IR DE(k) = WR(k)⌧4 R, D 2 P0, F1 2 P1, P2 , E 2 P0, P2 , (A15) ⌧4 R ⌘ 1 2⇡2 Z dq q2 W2 R(q)P2 mm(q). (A16) ntegrals (63) for E = F2 follow from Eqs. (A10) and (A12) by noting that only P2 0(⌫) ntegration over the product of Eqs. (A10) and (A12) because of Legendre polynomial G (k) = 1 4 WR(k)Pmm(k) ✓ 68 21 2 R 2 3 2 R,W 0 R ◆ , D 2 P0, F1 2 P1, P2 , (A17) 1 Z for D 2 {P0, F1 2 P1, P2} with hD[ R a ](k) R b For a 6= b, the bispectrum B Writing the smoothing kernels PD[ R a ], R b (k) = For cross-spectra of smooth 1 1 2 1 s exploited to fit b1, b2 and bs2 to the three cross-spectra at the same time. In practice, the fitted bias pa still be degenerate because sampling variance at low k and modeling uncertainty at high k limit the us In particular, in the range 0.01h/Mpc . k . 0.1h/Mpc, every cross-spectrum depends rather similarly o leading to a degeneracy where larger b1 can be compensated by a smaller b2, which is also present when individual bispectrum triangles rather than cross-spectra. Consequently, models that extend leading- higher k are expected to improve bias constraints significantly. In the large-scale limit, k ⌧ q, the three halo-halo-halo cross-spectra of Eq. (67) equal each other (see for details), lim k!0 PD[ R h ], R h (k) = WR(k)  b3 1Plin mm(k) ✓ 68 21 2 R 1 3 2 R,P 0 ◆ + 2b2 1b2 ⌧4 R + 2Plin mm(k) 2 R + 4 3 b2 1bs2 ⌧4 R , D 2 P0, F1 2 P where WR(k) ! 1 for k ! 0, and 2 R,P 0 and ⌧4 R are defined in Eqs. (A20) and (A16), respectively. the b3 1 term is proportional to the linear matter power spectrum, whereas the term involving b2 scales li matter power spectrum plus a k-independent correction and the term involving bs2 is entirely k-inde thin gray lines in Figs. 1 and 10 for the the ratio of these limits over the matter power spectrum). sampling variance at low k, this scale-dependence is expected to be less powerful in distinguishing bia than constraints obtained from the di↵erent scale-dependencies at high k. F. Shot noise The bispectrum of the smoothed halo density has an additional stochasticity contribution, h ˆBR hhh(k1, k2, k3)i = BR hhh(k1, k2, k3) + BR,shot hhh (k1, k2, k3), whose Poissonian prediction is (see e.g. [49]) BR,shot hhh (k1, k2, k3) = WR(k1)WR(k2)WR(k3) ⇢ 1 ¯nh [Phh(k1) + 2 perms] + 1 ¯n2 h . Different from position-dependent power spectrum (Chiang et al. 2014): where q1,3 refers to q1 and q3. Spherically averaging (ˆk · ˆq3)2 over k yields 1/3, and thus d2Ωˆk 4π B(k − q1, −k + q1 + q3, −q3) = 47 21 − 1 3 d ln P(k) d ln k P(k)P(q3) + O q1,3 k 2 = 68 21 − 1 3 d ln k3P(k) d ln k P(k)P(q3) + O q1,3 k 2 . (A.9) where q1,3 refers to q1 and q3. Spherically averaging (ˆk · ˆq3)2 over k yields 1/3, and thus d2Ωˆk 4π B(k − q1, −k + q1 + q3, −q3) = 47 21 − 1 3 d ln P(k) d ln k P(k)P(q3) + O q1,3 k 2 = 68 21 − 1 3 d ln k3P(k) d ln k P(k)P(q3) + O q1,3 k 2 . lim k!0
RECONSTRUCTION! BONUS! SLIDES!
GROWTH-SHIFT ALGORITHMS 2D slice plots: Components of EGS reconstruction MS et al. 1508.06972! rec EGS(x) = (x) (x) R(x) | {z } growth s(x) · r (x) | {z } shift random (middle) and F2 (right) algorithms. The panels show 2D slices of excess densities r density when performing reconstruction. The excess densities in the bottom panels are quadr measured from simulations. They coincide with the excess densities expected from modeling th n the upper panels with 2LPT, if the linear densities in Eqs. (46), (48) and (53) are replaced by and ERR, twice the excess density is shown to enhance color contrast. We use R = 15h 1 Mpc reconstructions. To highlight large scales, each final field is additionally smoothed externally wit The clustered catalog is obtained from a 1% random subsample of one RunPB N-body realizat equally many randomly distributed particles. FIG. 11. Left panel: Nonlinear density before reconstruction (divided by 3 for better visibility parison of Lagrangian (top) and Eulerian (bottom) reconstructions at the field level for growth-shift (left), rand le) and F2 (right) algorithms. The panels show 2D slices of excess densities rec that are removed from performing reconstruction. The excess densities in the bottom panels are quadratic in the nonlinear den m simulations. They coincide with the excess densities expected from modeling the Lagrangian reconstruct anels with 2LPT, if the linear densities in Eqs. (46), (48) and (53) are replaced by the nonlinear ones. For L ce the excess density is shown to enhance color contrast. We use R = 15h 1 Mpc Gaussian smoothing for s. To highlight large scales, each final field is additionally smoothed externally with a R = 15h 1 Mpc Gauss catalog is obtained from a 1% random subsample of one RunPB N-body realization; the random catalog f randomly distributed particles. panel: Nonlinear density before reconstruction (divided by 3 for better visibility). Middle panels: Individ ift contributions to the Eulerian growth-shift reconstruction. Right panel: Expected excess density for grow
ALL ALGORITHMS Performance comparison! EGS algorithm yields 95% of BAO S/N of standard Lagrangian LGS algorithm! EF2 algorithm similar, other algorithms significantly worse MS et al. 1508.06972! FIG. 9. Cumulative BAO signal-to-noise-squared for reconstructed power spectra as a function of kmax measured from sim- ulations for three Eulerian reconstruction algorithms (dashed colored) and the corresponding three Eulerian reconstruction algorithms (solid colored). See Section V for a summary of the algorithms. The signal-to-noise-squared of the density before reconstruction (black) and of the linear density (gray) are included for comparison. Growth-Shift F2 reconstruction Random-Random Reconstruction method LGS EGS LF2 EF2 LRR ERR Perfect NoRec BAO signal-to-noise 14.2 13.6 12.7 13.3 11.1 11.3 17.0 10.3 Compared against LGS ±0 % 4.7 % 11 % 6.3 % 22 % 21 % +19 % 27 % Compared against NoRec +38 % +31 % +23 % +29 % +6.9 % +8.8 % +64 % ±0 % TABLE II. Total BAO signal-to-noise for kmax = 0.4h/Mpc for various reconstruction algorithms (obtained from Fig. 9, based on simulations). ‘Perfect’ refers to the BAO signal-to-noise of the linear density and ‘NoRec’ to the BAO signal-to-noise of the measured nonlinear density without performing any reconstruction. The second-to-last row shows how much of the signal- to-noise is lost compared to performing the standard LGS reconstruction. The bottom row shows how much signal-to-noise is gained by reconstructions compared to performing no reconstruction. The EGS and EF2 methods may be useful for applications because they perform almost as well as the standard LGS algorithm and have potential advantages due to their Eulerian nature (e.g. they can be expressed as combinations of 2-, 3- and 4-point functions of the unreconstructed density). The LF2 method and the random-random methods are not competitive in terms of signal-to-noise so they should not be used. The performance of ERR can be improved by using asymmetric smoothing where only one of the fields entering quadratic fields is smoothed; in this case ERR performs almost as well as EF2 but still slightly worse. For the LRR method it is less obvious how to reduce the
SMOOTHING SCALE Dependence on smoothing scale MS et al. 1508.06972! 2. Cumulative BAO signal-to-noise for varying Gaussian smoothing scale R, using scales up to kmax = 0.15h/M x = 0.4h/Mpc (right). Note that the shot noise in our setup is unrealistically small, ¯n 1 = 31Mpc3 /h3 .

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Large-Scale Structure beyond the Power Spectrum

  • 1. LARGE-SCALE STRUCTURE! BEYOND! THE POWER SPECTRUM Marcel Schmittfull! ! Berkeley Center for Cosmological Physics (BCCP)! ! arxiv:1411.6595 (PRD 91, 043530) ! arxiv:1508.06972! ! 
 In collaboration with! Tobias Baldauf, Uros Seljak
 Yu Feng, Florian Beutler, Blake Sherwin, Man Yat Chu! ! Berkeley BCCP Seminar, Sep 8 2015
  • 2. Introduction! Part I: Simple bispectrum estimators! Part II: Eulerian reconstructions and N-point statistics OVERVIEW
  • 4. MAIN COSMOLOGICAL PROBES Young universe (380,000 yrs after big bang)! [Gravity waves] ! Cosmic Microwave Background (CMB)! ! ‘Recent’ universe (billions yrs after big bang)! Large-scale structure (LSS)! 21cm, Lyα, CMB lensing, galaxy clustering, weak lensing! Supernovae All observations can be described with LCDM cosmological model 
 (start with inflation, then expand with CDM and cosmological constant dark energy) Dana Berry / SkyWorks Digital Inc. and the SDSS collaboration
  • 5. MAIN COSMOLOGICAL PROBES Constraints on 6-parameter LCDM model:! CMB is most powerful! 24 Florian Beutler et al. mΩ 0.1 0.2 0.3 0.4 0.5 0.6 0.7 8 σ 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 CDM)ΛPlanck ( )8 σCMASS (f Our analysis )8 σ,f AP CMASS (F Our analysis Planck SZ clusters Ade et al. (2013) CFHTLenS Kilbinger et al. (2013) )8 σ,f AP ,Fs/rv CMASS (D s Planck Our analysis + r )8 σ,f AP ,Fs/rv CMASS (D s WMAP9 Our analysis + r Figure 17. Comparison between Planck (Ade et al. 2013a), Beutler et al 2013 DM fraction of total energy budget in the universe Magnitudeoffluctuations
  • 6. MAIN COSMOLOGICAL PROBES Constraints on 6-parameter LCDM model:! CMB is most powerful! Free neutrino mass Σmν! Constraints degrade because Σmν and σ8 degenerate in CMB! LSS helps DM fraction of total energy budget in the universe Magnitudeoffluctuations 10 Florian Beutler et al. mΩ 0.25 0.3 0.35 0.4 0.45 0.5 8 σ 0.65 0.7 0.75 0.8 0.85 )νmΣPlanck (free )νmΣPlanck+CMBlensing (free CFHTLenS Kilbinger et al. (2013) Planck+Beutler2013 Planck+Beutler2013+CFHTLenS Beutler et al 2013
  • 7. MAIN COSMOLOGICAL PROBES Constraints on 6-parameter LCDM model:! CMB is most powerful! Free neutrino mass Σmν! Constraints degrade because Σmν and σ8 degenerate in CMB! LSS helps! Sum of neutrino masses Magnitudeoffluctuations utler et al. mΩ 0.35 0.4 0.45 0.5 )νmΣPlanck (free )νmΣPlanck+CMBlensing (free CFHTLenS Kilbinger et al. (2013) Planck+Beutler2013 Planck+Beutler2013+CFHTLenS [eV]νmΣ 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 8σ 0.65 0.7 0.75 0.8 0.85 )νmΣPlanck (free Planck+Beutler2013 Planck+Beutler2013+CFHTLenS Beutler et al 2013
  • 8. MAIN COSMOLOGICAL PROBES Constraints on 6-parameter LCDM model:! CMB is most powerful! Free neutrino mass Σmν! Constraints degrade because Σmν and σ8 degenerate in CMB! LSS helpful! Other model extensions (curvature, time-varying dark energy, mod. grav. etc)! LSS crucial Beutler et al 2013 Dana Berry / SkyWorks Digital Inc. and the SDSS collaboration Generally, should not just compare constraining power: LSS checks cosmological model as a whole (low z vs high z from CMB)
  • 10. @⌘ + r[(1 + )v)] = 0 Equations of motion for DM are non-linear, containing products of Gaussian first- order perturbations (e.g. continuity equation )! ➟ DM density is not Gaussian (even in absence of primordial non-Gaussianity): kmax ~ 0.15 h/Mpc, z = 0.55, 20483 DM ptcles DARK MATTER DENSITY
  • 11. NON-GAUSSIANITY FROM GRAVITY Another complication:! Distribution of dark matter is different from that of galaxies ➟ “Galaxy bias”! ! !
  • 12. NON-GAUSSIANITY FROM GRAVITY Another complication:! Distribution of dark matter is different from that of galaxies ➟ “Galaxy bias”! ! ! night human population density Adopted from Tobias Baldauf
  • 13. NON-GAUSSIANITY FROM GRAVITY Another complication:! Distribution of dark matter is different from that of galaxies ➟ “Galaxy bias”! ! ! night human population density Adopted from Tobias Baldauf
  • 14. Galaxies are biased tracer of DM! Bias relation has additional non-linearities! ➟ pdf of galaxy density is even more non-Gaussian than that for DM: kmax ~ 0.15 h/Mpc, z = 0.55, halos with b1~2 and mass 1-4 x 1013 Msolar/h GALAXY DENSITY g(x) ⇠ b1 m(x) + b2 2 m(x) + bs2 s2 m(x)
  • 16. Gaussian field is completely specified by its power spectrum Pδ 
 (2-point correlation function in Fourier space)
 
 
 
 
 
 Primary diagnostic for non-Gaussianity is the bispectrum Bδ 
 (3-point correlation function in Fourier space:
 given 2 over-densities, specifies probability of having a third overdensity)! ! ! ! ! POWER AND BISPECTRUM h (k1) (k2)i = (2⇡)3 D(k1 + k2)P (k1) bispectrum drawn on space of triangle configurations = (2⇡)3 D(k1 + k2 + k3)B (k1, k2, k3) h (k1) (k2) (k3)i 2.4 Large-scale dark matter perturbations 10 3 10 2 10 1 100 k [Mpc 1] 101 102 103 104 105 P(k,z=0)[Mpc3] SDSS DR7 (Reid et al. 2010) LyA (McDonald et al. 2006) ACT CMB Lensing (Das et al. 2011) ACT Clusters (Sehgal et al. 2011) CCCP II (Vikhlinin et al. 2009) BCG Weak lensing (Tinker et al. 2011) ACT+WMAP spectrum (this work) 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 Mass scale M [Msolar] 10 6 10 5 10 4 10 3 10 2 10 1 100 101 102 MassVarianceM/M SDSS DR7 (Reid et al. 2010) LyA (McDonald et al. 2006) ACT CMB Lensing (Das et al. 2011) ACT Clusters (Sehgal et al. 2011) CCCP II (Vikhlinin et al. 2009) BCG Weak lensing (Tinker et al. 2011) ACT+WMAP spectrum (this work) Fig. 5.— The reconstructed matter power spectrum: the stars show the power spectrum from combining ACT and WMAP data (to panel). The solid and dashed lines show the nonlinear and linear power spectra respectively from the best-fit ACT ⇤CDM model wit spectral index of ns = 0.96 computed using CAMB and HALOFIT (Smith et al. 2003). The data points between 0.02 < k < 0.19 Mpc show the SDSS DR7 LRG sample, and have been deconvolved from their window functions, with a bias factor of 1.18 applied to the dat This has been rescaled from the Reid et al. (2010) value of 1.3, as we are explicitly using the Hubble constant measurement from Riess et a (2011) to make a change of units from h 1Mpc to Mpc. The constraints from CMB lensing (Das et al. 2011), from cluster measurement from ACT (Sehgal et al. 2011), CCCP (Vikhlinin et al. 2009) and BCG halos (Tinker et al. 2011), and the power spectrum constrain from measurements of the Lyman–↵ forest (McDonald et al. 2006) are indicated. The CCCP and BCG masses are converted to solar ma units by multiplying them by the best-fit value of the Hubble constant, h = 0.738 from Riess et al. (2011). The bottom panel shows th (Hlozek et al. 2011) ~ keq Figure 2.8: Reconstructed matter power spectrum today, adapted from Fig. 5 in [117]. The data points are from various large-scale structure and CMB ex- periments. The dashed line represents the linear power spectrum which follows approximately (2.120). The solid line contains non-linear corrections which are important on small scales. The integral can be computed numerically using the Friedmann equation ✓ H(a) H0 ◆2 = ⌦ra 4 + ⌦ma 3 + ⌦⇤ + ⌦ka 2 , (2.122) where ⌦X are the density parameters today (when a = 1). Note that we only consider the fastest growing mode here and in the following. 2.4.4 Higher-order perturbation theory The late-time dark matter distribution has a non-trivial bispectrum even in the absence of primordial non-Gaussianity due to non-linearities in the equations of 59 Hlozek et al. 2012
  • 17. MOTIVATION FOR MEASURING BISPECTRUM Early universe: Constrain primordial non-Gaussianity! CMB close to cosmic variance limit, so need LSS ! : Bispectrum complementary to P(k) and less affected by low-k systematics! : No signal in P(k) → need bispectrum! e.g. SPHEREX (proposed satellite) floc NL feq NL Late universe: Break parameter degeneracies, e.g. between b1 and σ8:! Power spectrum: ! Bispectrum: ! Lots of upcoming LSS experiments that could benefit, e.g. eBOSS, DESI, EUCLID, WFIRST, LSST, … Phh / b2 1 2 8 Bhhh /⇠ b3 1 4 8+ ⇠ b2 1b2 4 8 + · · · Fry 1994! Verde et al. 1997-2002! Scoccimarro et al. 1998! Sefusatti et al. 2006! Gil-Marin et al. 2014 Doré et al. 2014
  • 18. MOTIVATION FOR MEASURING BISPECTRUM Early universe: Constrain primordial non-Gaussianity! CMB close to cosmic variance limit, so need LSS ! : Bispectrum complementary to P(k) and less affected by low-k systematics! : No signal in P(k) → need bispectrum! e.g. SPHEREX (proposed satellite) floc NL feq NL Late universe: Break parameter degeneracies, e.g. between b1 and σ8:! Power spectrum: ! Bispectrum: ! Lots of upcoming LSS experiments that could benefit, e.g. eBOSS, DESI, EUCLID, WFIRST, LSST, … Phh / b2 1 2 8 Bhhh /⇠ b3 1 4 8+ ⇠ b2 1b2 4 8 + · · · Fry 1994! Verde et al. 1997-2002! Scoccimarro et al. 1998! Sefusatti et al. 2006! Gil-Marin et al. 2014 But: Beyond 2-point plagued by increased complexity of analysis
 (covariances, window function, fiber collisions, computational cost ~ N6; 
 non-linear DM, bias, redshift-space distortions, galaxy-halo connection, …) Doré et al. 2014
  • 20. Non-linear DM density (2nd order SPT)! ! ! ! ! ! ! ! THEORY DM BISPECTRUM m(x) = 0(x) | {z } linear + 17 21 2 0(x) | {z } nonlinear growth + 0(x) · r 0(x) | {z } shift + 4 21 s2 0(x) | {z } tidal ) h m(k1) m(k2) m(k3)i ⇠ 2Plin mm(k1)Plin mm(k2)F2(k1, k2) + 2 perms in k1, k2, k3 F2(k1, k2) = 17 21|{z} nonlinear growth + 1 2 ✓ k1 k2 + k2 k1 ◆ ˆk1 · ˆk2 | {z } shift + 4 21 3 2 ✓ (ˆk1 · ˆk2)2 1 3 ◆ | {z } tidal δm: nonlinear DM density
 δ0: linear DM density
 : linear displacement
 s0: linear tidal tensor ⌘ ik k2 0(k)0 where
  • 21. Non-linear halo bias! ! ! ! ! ! ! ! THEORY HALO BISPECTRUM δm: nonlinear DM density
 δh: halo density
 sm: DM tidal tensor h(x) = b1 m(x) + b2 ⇥ 2 m(x) h 2 m(x)i ⇤ + 2 3 bs2 ⇥ s2 m(x) hs2 m(x)i ⇤ ) h h(k1) h(k2) h(k3)i ⇠ 2Plin mm(k1)Plin mm(k2)  b3 1F2(k1, k2) + b2 1b2 + 2 3 b2 1bs2 P2(ˆk1 · ˆk2) + 2 perms
  • 22. Non-linear halo bias! ! ! ! ! ! ! ! THEORY HALO BISPECTRUM δm: nonlinear DM density
 δh: halo density
 sm: DM tidal tensor h(x) = b1 m(x) + b2 ⇥ 2 m(x) h 2 m(x)i ⇤ + 2 3 bs2 ⇥ s2 m(x) hs2 m(x)i ⇤ ) h h(k1) h(k2) h(k3)i ⇠ 2Plin mm(k1)Plin mm(k2)  b3 1F2(k1, k2) + b2 1b2 + 2 3 b2 1bs2 P2(ˆk1 · ˆk2) + 2 perms Decompose in Legendre polys! ! ! ! ! ! ! ! Pl with l = 0, 1, 2 B (l=0) hhh = ✓ 34 21 b3 1 + 2b2 1b2 ◆ Plin mm(k1)Plin mm(k2) + 2 perms, B (l=1) hhh = b3 1 ✓ k1 k2 + k2 k1 ◆ Plin mm(k1)Plin mm(k2)P1(ˆk1 · ˆk2) + 2 perms, B (l=2) hhh = ✓ 8 21 b3 1 + 4 3 b2 1bs2 ◆ Plin mm(k1)Plin mm(k2)P2(ˆk1 · ˆk2) + 2 perms.
  • 23. BISPECTRUM ESTIMATION Goal: Given DM/halo density δ, estimate coefficients of all bispectrum contributions! ➟ these depend on bias and cosmological parameters that we aim to extract! Method: Maximum likelihood estimators for the coefficient of contribution Bcontri
 
 
 
 
 Example: Bcontri = P(k1)P(k2) 
 
 
 
 " ! ! ! ˆcoe↵(Bcontri | ) / Z k,q Bcontri (q, k q, k) | {z } theory template (q) (k q) ( k) P (q)P (|k q|)P (k) | {z } inv. variance weighted data MS, Baldauf, Seljak, 1411.6595 = = cross-spectrum of δ2(x) and δ! ) ˆcoe↵ / Z dk k2 P(k) ˆP 2, (k) 0 h 2 | i0 ˆP 2, (k) ⇠ X k,|k|=k [ 2 ](k) ( k) relies on separability of! LSS bispectrum
  • 24. MS, Baldauf, Seljak, 1411.6595 l=0
 growth • nonlinear DM growth δ2! • quadratic bias b2 squared density X density ˆP 2, (k) ⇠ X k,|k|=k [ 2 ](k) ( k) • nonlinear DM shift (x) · r (x) i (k) ⌘ iki k2 (k) l=1
 shift displacement dot
 density gradient X density ˆP i@i , (k) ⇠ X k,|k|=k [ i @i ](k) ( k) A. Qua As will be shown in Section III, maximum-likelihood form of cross-spectra of the density field with three field dependencies on the cosine µ between the Fourier space µ ⌘ q Explicitly, these three quadratic fields are: • The squared density 2 (x), which can be written a 2 (x) = Z d3 k (2⇡)3 eik where P0(µ) = 1 is the Legendre polynomial for l = • The shift-term i (x)@i (x) = (x) · r (x) = Z which depends on the l = 1 Legendre polynomial P r with the displacement field (k The symmetric kernel F1 2 in Eq. (3) is defined as F1 2 (k1, k2 • The tidal term3 s2 (x) ⌘ 3 2 sij(x)sij(x) = Z which is defined by contracting the tidal tensor sij(k) = ✓ with itself. (K) denotes the Kronecker delta. The dependencies on the cosine µ between the Fourier space wavevectors q and k q µ ⌘ q · (k q) q|k q| . Explicitly, these three quadratic fields are: • The squared density 2 (x), which can be written as a convolution in Fourier 2 (x) = Z d3 k (2⇡)3 eikx Z d3 q (2⇡)3 P0(µ) (q) (k where P0(µ) = 1 is the Legendre polynomial for l = 0.2 • The shift-term i (x)@i (x) = (x) · r (x) = Z d3 k (2⇡)3 eikx Z d3 q (2⇡)3 F1 2 (q, |k which depends on the l = 1 Legendre polynomial P1(µ) = µ and is obtained r with the displacement field (k) = ik k2 (k). The symmetric kernel F1 2 in Eq. (3) is defined as F1 2 (k1, k2) = 1 2 ✓ k1 k2 + k2 k1 ◆ . • The tidal term3 s2 (x) ⌘ 3 2 sij(x)sij(x) = Z d3 k (2⇡)3 eikx Z d3 q (2⇡)3 P2(µ) which is defined by contracting the tidal tensor sij(k) = ✓ kikj k2 1 3 (K) ij ◆ (k) with itself. (K) ij denotes the Kronecker delta. The corresponding convolutio l = 2 Legendre polynomial • nonlinear DM tidal term! • tidal tensor bias bs2 l=2
 tidal ˆPs2, (k) ⇠ X k,|k|=k [s2 ](k) ( k) tidal tensor X density GET ALL BISPECTRUM CONTRIS 
 FROM 3 CROSS-SPECTRA
  • 25. IN PICTURES MS, Baldauf, Seljak, 1411.6595 1. Start with density! (x)y[Mpc/h] x [Mpc/h]
  • 26. IN PICTURES MS, Baldauf, Seljak, 1411.6595 y[Mpc/h] x [Mpc/h] R(x) 2. Apply smoothing (here Gaussian with R=20 Mpc/h)!
  • 27. IN PICTURES MS, Baldauf, Seljak, 1411.6595 y[Mpc/h] x [Mpc/h] 2 R(x) 3. Get squared density!
  • 28. IN PICTURES MS, Baldauf, Seljak, 1411.6595 y[Mpc/h] x [Mpc/h] R(x) · r R(x) 4. Get shift term!
  • 29. IN PICTURES MS, Baldauf, Seljak, 1411.6595 y[Mpc/h] x [Mpc/h] s2 R(x) 5. Get tidal term
  • 30. l=0
 growth • nonlinear DM 
 growth δ2! • quadratic bias b2 squared density density ˆP 2, (k) ⇠ X k,|k|=k [ 2 ](k) ( k) • nonlinear DM shift (x) · r (x) l=1
 shift displacement dot
 density gradient density ˆP i@i , (k) ⇠ X k,|k|=k [ i @i ](k) ( k) • nonlinear DM 
 tidal term! • tidal tensor bias l=2
 tidal ˆPs2, (k) ⇠ X k,|k|=k [s2 ](k) ( k) tidal tensor density IN PICTURES 6. Cross-correlate MS, Baldauf, Seljak, 1411.6595 X X X ⇠ ⇠ ⇠ k [Mpc/h] k [Mpc/h] k [Mpc/h]
  • 31. PRIMORDIAL NON-GAUSSIANITY Optimal estimator from DM bispectrum: MS, Baldauf, Seljak, 1411.6595 VI. EXTENSION TO PRIMORDIAL NON-GAUSSIANITY So far we have assumed Gaussian initial conditions. Multiple-field inflation models can generate loc non-Gaussianity that induces an additional contribution to the matter-matter-matter bispectrum of th Bloc mmm(k1, k2, k3) = 2floc NL  M(k3) M(k1)M(k2) Pmm(k1)Pmm(k2) + 2 perms , where M(k) = M(k, z) is the linear Poisson conversion factor between the primordial potential and matter density at redshift z, M(k, z) ⌘ 2 3 k2 T(k)D(z) ⌦mH2 0 , so that lin m (k, z) = M(k, z) (k). Here, T(k) is the linear transfer function normalized to T(k) = 1 on and the linear growth factor D(z) for ⌦rad = 0 is normalized to D(z) = 1/(1 + z) during matter dom that M(k) / k2 for k ⌧ keq and M(k) / k0 for k keq. The bispectrum (82) is maximal in the s (e.g. k1 ⌧ k2 ⇡ k3). Plugging the bispectrum (82) into Eq. (36), we get ˆfloc NL = 24⇡L3 Nloc Z dk k2 M2 (k) Pmm(k) ˆP[ m M ]2, m M (k), where we defined the quadratic field  m M 2 (k) ⌘ Z d3 q (2⇡)3 m(q) M(q) m(k q) M(|k q|) M(k, z) ⌘ 2 3 k2 T(k)D(z) ⌦mH2 0 , (83) k, z) (k). Here, T(k) is the linear transfer function normalized to T(k) = 1 on large scales, actor D(z) for ⌦rad = 0 is normalized to D(z) = 1/(1 + z) during matter domination. Note ⌧ keq and M(k) / k0 for k keq. The bispectrum (82) is maximal in the squeezed limit rum (82) into Eq. (36), we get ˆfloc NL = 24⇡L3 Nloc Z dk k2 M2 (k) Pmm(k) ˆP[ m M ]2, m M (k), (84) uadratic field  m M 2 (k) ⌘ Z d3 q (2⇡)3 m(q) M(q) m(k q) M(|k q|) (85) m M (k) ⌘ m(k) M(k) . (86) quals the primordial potential reconstructed from the DM density m. The cross-spectrum the cross-spectrum of this reconstructed with 2 (x), which corresponds to the mechanism al non-Gaussianity of the local kind (adding fNL 2 (x) to (x)). appearing in Eq. (84) could be used to estimate floc NL if the dark matter density was directly ion to observable halo densities is left for future work. It would also be straightforward to a to other separable types of primordial non-Gaussianity generated by other inflation models ogonal). (Poisson factor) ➟ basically just ɸ2 cross ɸ, where ɸ ~ δ/k2! ➟ in principle same S/N as getting from measurement of the entire LSS bispectrum! floc NL floc NL
  • 33. MODELING CROSS-SPECTRA Expectation value of cross-spectrum with general kernel ! ! ! MS, Baldauf, Seljak, 1411.6595 integrated# bispectrum for D 2 {P0, F1 2 P1, P2} with the den hD[ R a ](k) R b (k0 )i = = For a 6= b, the bispectrum B is not s Writing the smoothing kernels explicit PD[ R a ], R b (k) = WR(k) C For cross-spectra of smoothed dark PD[ R m], R m where we used that the kernel D(q, k hD[ R a ](k) R b (k0 )i = (2⇡)3 D(q, k q)h R a (q) R a (k q) R b (k0 )i = (2⇡)3 D(k + k0 ) Z d3 q (2⇡)3 D(q, k q)B R a R a R b (q, k q, k). = b, the bispectrum B is not symmetric in its arguments and the last argument k is associated w g the smoothing kernels explicitly, we have PD[ R a ], R b (k) = WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)D(q, k q)B a a b (q, k q, k). C. Matter-matter-matter cross-spectra cross-spectra of smoothed dark matter fields (a = b = m), the DM bispectrum (16) gives PD[ R m], R m (k) = Z d3 q (2⇡)3 D(q, k q)B R m R m R m (q, k q, k) = 2IR DF2 (k) + 4Ibare,R DF2 (k), we used that the kernel D(q, k q) is assumed to be symmetric in its arguments and we defined for ker IR DE(k) ⌘ WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)Plin mm(|k q|)D(q, k q)E(q, k q), s symmetric under D $ E, and Ibare,R DE (k) ⌘ WR(k)Plin mm(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)D(q, k q)E(q, k),
  • 34. MODELING CROSS-SPECTRA Expectation value of cross-spectrum with general kernel ! ! ! MS, Baldauf, Seljak, 1411.6595 integrated# bispectrum for D 2 {P0, F1 2 P1, P2} with the den hD[ R a ](k) R b (k0 )i = = For a 6= b, the bispectrum B is not s Writing the smoothing kernels explicit PD[ R a ], R b (k) = WR(k) C For cross-spectra of smoothed dark PD[ R m], R m where we used that the kernel D(q, k hD[ R a ](k) R b (k0 )i = (2⇡)3 D(q, k q)h R a (q) R a (k q) R b (k0 )i = (2⇡)3 D(k + k0 ) Z d3 q (2⇡)3 D(q, k q)B R a R a R b (q, k q, k). = b, the bispectrum B is not symmetric in its arguments and the last argument k is associated w g the smoothing kernels explicitly, we have PD[ R a ], R b (k) = WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)D(q, k q)B a a b (q, k q, k). C. Matter-matter-matter cross-spectra cross-spectra of smoothed dark matter fields (a = b = m), the DM bispectrum (16) gives PD[ R m], R m (k) = Z d3 q (2⇡)3 D(q, k q)B R m R m R m (q, k q, k) = 2IR DF2 (k) + 4Ibare,R DF2 (k), we used that the kernel D(q, k q) is assumed to be symmetric in its arguments and we defined for ker IR DE(k) ⌘ WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)Plin mm(|k q|)D(q, k q)E(q, k q), s symmetric under D $ E, and Ibare,R DE (k) ⌘ WR(k)Plin mm(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)D(q, k q)E(q, k), 10 for D 2 {P0, F1 2 P1, P2} with the density R b is given by an integral over the bispectrum B, hD[ R a ](k) R b (k0 )i = Z d3 q (2⇡)3 D(q, k q)h R a (q) R a (k q) R b (k0 )i (57) = (2⇡)3 D(k + k0 ) Z d3 q (2⇡)3 D(q, k q)B R a R a R b (q, k q, k). (58) For a 6= b, the bispectrum B is not symmetric in its arguments and the last argument k is associated with b. Writing the smoothing kernels explicitly, we have PD[ R a ], R b (k) = WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)D(q, k q)B a a b (q, k q, k). (59) C. Matter-matter-matter cross-spectra For cross-spectra of smoothed dark matter fields (a = b = m), the DM bispectrum (16) gives PD[ R m], R m (k) = Z d3 q (2⇡)3 D(q, k q)B R m R m R m (q, k q, k) (60) = 2IR DF2 (k) + 4Ibare,R DF2 (k), (61) where we used that the kernel D(q, k q) is assumed to be symmetric in its arguments and we defined for kernels D and E IR DE(k) ⌘ WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)Plin mm(|k q|)D(q, k q)E(q, k q), (62) which is symmetric under D $ E, and Ibare,R DE (k) ⌘ WR(k)Plin mm(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)D(q, k q)E(q, k), (63) which is not symmetric under D $ E. Explicit predictions for the cross-spectra P 2 m, m , P i m@i m, m and Ps2 m, m can be obtained from Eq. (61) by plugging in D = P0, D = F1 2 P1 and D = P2, respectively, and setting E = F2. Note that there are three powers of the smoothing kernel because we smooth the nonlinear rather than the linear field. The integrals in Eqs. (62) and (63) are similar to typical 1-loop expressions and can be reduced to two-dimensional integrals over scale q and cosine ˆq·ˆk, which can be evaluated numerically with little computational cost (see e.g. [43, 44] for public codes that compute similar integrals). The factors WR(|k q|) and Plin mm(|k q|) introduce a non-trivial angle dependence so that the angular integration generally needs to be performed numerically. 10 for D 2 {P0, F1 2 P1, P2} with the density R b is given by an integral over the bispectrum B, hD[ R a ](k) R b (k0 )i = Z d3 q (2⇡)3 D(q, k q)h R a (q) R a (k q) R b (k0 )i (57) = (2⇡)3 D(k + k0 ) Z d3 q (2⇡)3 D(q, k q)B R a R a R b (q, k q, k). (58) For a 6= b, the bispectrum B is not symmetric in its arguments and the last argument k is associated with b. Writing the smoothing kernels explicitly, we have PD[ R a ], R b (k) = WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)D(q, k q)B a a b (q, k q, k). (59) C. Matter-matter-matter cross-spectra For cross-spectra of smoothed dark matter fields (a = b = m), the DM bispectrum (16) gives PD[ R m], R m (k) = Z d3 q (2⇡)3 D(q, k q)B R m R m R m (q, k q, k) (60) = 2IR DF2 (k) + 4Ibare,R DF2 (k), (61) where we used that the kernel D(q, k q) is assumed to be symmetric in its arguments and we defined for kernels D and E IR DE(k) ⌘ WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)Plin mm(|k q|)D(q, k q)E(q, k q), (62) which is symmetric under D $ E, and Ibare,R DE (k) ⌘ WR(k)Plin mm(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)D(q, k q)E(q, k), (63) which is not symmetric under D $ E. Explicit predictions for the cross-spectra P 2 m, m , P i m@i m, m and Ps2 m, m can be obtained from Eq. (61) by plugging in D = P0, D = F1 2 P1 and D = P2, respectively, and setting E = F2. Note that there are three powers of the smoothing kernel because we smooth the nonlinear rather than the linear field. The integrals in Eqs. (62) and (63) are similar to typical 1-loop expressions and can be reduced to two-dimensional integrals over scale q and cosine ˆq·ˆk, which can be evaluated numerically with little computational cost (see e.g. [43, 44] for public codes that compute similar integrals). The factors WR(|k q|) and Plin mm(|k q|) introduce a non-trivial angle dependence so that the angular integration generally needs to be performed numerically. The only ingredient for the theory prediction of Eq. (61) is the model for the DM bispectrum. Improved bispectrum Leading-order SPT for hhh cross-spectra! alo bispectrum (28) gives for the halo-halo-halo cross-spectra 3 1 h IR DF2 (k) + 2Ibare,R DF2 (k) i + 2b2 1b2 h IR DP0 (k) + 2Ibare,R DP0 (k) i + 4 3 b2 1bs2 h IR DP2 (k) + 2Ibare,R DP2 (k) i . (67) ending on b3 1 also appear in matter-matter-halo cross spectra, so that PD[ R h ], R h (k) b2 1PD[ R m], R h (k) = 4b2 1  b2Ibare,R DP0 (k) + 2 3 bs2 Ibare,R DP2 (k) . (68) F2 kernel in Legendre polynomials as in Eq. (18), Eq. (67) can be rewritten as PD[ R h ], R h (k) = ✓ 34 21 b3 1 + 2b2 1b2 ◆ h IR DP0 (k) + 2Ibare,R DP0 (k) i + 2b3 1 h IR D,F 1 2 P1 (k) + 2Ibare,R D,F 1 2 P1 (k) i + ✓ 8 21 b3 1 + 4 3 b2 1bs2 ◆ h IR DP2 (k) + 2Ibare,R DP2 (k) i . (69) ns to the theory expression of Eq. (67) are shown in Fig. 1 for Gaussian smoothing with R = g. 10 in the appendix for R = 10h 1 Mpc). Di↵erent colors describe di↵erent cross-spectra, P2}, while di↵erent line styles correspond to the contributions with di↵erent dependencies on
  • 35. MODELING CROSS-SPECTRA Expectation value of cross-spectrum with general kernel ! ! ! MS, Baldauf, Seljak, 1411.6595 integrated# bispectrum for D 2 {P0, F1 2 P1, P2} with the den hD[ R a ](k) R b (k0 )i = = For a 6= b, the bispectrum B is not s Writing the smoothing kernels explicit PD[ R a ], R b (k) = WR(k) C For cross-spectra of smoothed dark PD[ R m], R m where we used that the kernel D(q, k hD[ R a ](k) R b (k0 )i = (2⇡)3 D(q, k q)h R a (q) R a (k q) R b (k0 )i = (2⇡)3 D(k + k0 ) Z d3 q (2⇡)3 D(q, k q)B R a R a R b (q, k q, k). = b, the bispectrum B is not symmetric in its arguments and the last argument k is associated w g the smoothing kernels explicitly, we have PD[ R a ], R b (k) = WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)D(q, k q)B a a b (q, k q, k). C. Matter-matter-matter cross-spectra cross-spectra of smoothed dark matter fields (a = b = m), the DM bispectrum (16) gives PD[ R m], R m (k) = Z d3 q (2⇡)3 D(q, k q)B R m R m R m (q, k q, k) = 2IR DF2 (k) + 4Ibare,R DF2 (k), we used that the kernel D(q, k q) is assumed to be symmetric in its arguments and we defined for ker IR DE(k) ⌘ WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)Plin mm(|k q|)D(q, k q)E(q, k q), s symmetric under D $ E, and Ibare,R DE (k) ⌘ WR(k)Plin mm(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)D(q, k q)E(q, k), 13 G. Covariances as and cosmological parameters from cross-spectra we need to know their noise and covariance ng-order perturbation theory predicts for the covariance between two cross-spectra at the same Appendix B) cov( ˆPD[ R a ], R b (k), ˆPE[ R a ], R b (k)) = 2 Nmodes(k) PR bb(k)I P R aa P R aa DE (k), (78) Covariance between cross-spectra at the same k 10 for D 2 {P0, F1 2 P1, P2} with the density R b is given by an integral over the bispectrum B, hD[ R a ](k) R b (k0 )i = Z d3 q (2⇡)3 D(q, k q)h R a (q) R a (k q) R b (k0 )i (57) = (2⇡)3 D(k + k0 ) Z d3 q (2⇡)3 D(q, k q)B R a R a R b (q, k q, k). (58) For a 6= b, the bispectrum B is not symmetric in its arguments and the last argument k is associated with b. Writing the smoothing kernels explicitly, we have PD[ R a ], R b (k) = WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)D(q, k q)B a a b (q, k q, k). (59) C. Matter-matter-matter cross-spectra For cross-spectra of smoothed dark matter fields (a = b = m), the DM bispectrum (16) gives PD[ R m], R m (k) = Z d3 q (2⇡)3 D(q, k q)B R m R m R m (q, k q, k) (60) = 2IR DF2 (k) + 4Ibare,R DF2 (k), (61) where we used that the kernel D(q, k q) is assumed to be symmetric in its arguments and we defined for kernels D and E IR DE(k) ⌘ WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)Plin mm(|k q|)D(q, k q)E(q, k q), (62) which is symmetric under D $ E, and Ibare,R DE (k) ⌘ WR(k)Plin mm(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)D(q, k q)E(q, k), (63) which is not symmetric under D $ E. Explicit predictions for the cross-spectra P 2 m, m , P i m@i m, m and Ps2 m, m can be obtained from Eq. (61) by plugging in D = P0, D = F1 2 P1 and D = P2, respectively, and setting E = F2. Note that there are three powers of the smoothing kernel because we smooth the nonlinear rather than the linear field. The integrals in Eqs. (62) and (63) are similar to typical 1-loop expressions and can be reduced to two-dimensional integrals over scale q and cosine ˆq·ˆk, which can be evaluated numerically with little computational cost (see e.g. [43, 44] for public codes that compute similar integrals). The factors WR(|k q|) and Plin mm(|k q|) introduce a non-trivial angle dependence so that the angular integration generally needs to be performed numerically. 10 for D 2 {P0, F1 2 P1, P2} with the density R b is given by an integral over the bispectrum B, hD[ R a ](k) R b (k0 )i = Z d3 q (2⇡)3 D(q, k q)h R a (q) R a (k q) R b (k0 )i (57) = (2⇡)3 D(k + k0 ) Z d3 q (2⇡)3 D(q, k q)B R a R a R b (q, k q, k). (58) For a 6= b, the bispectrum B is not symmetric in its arguments and the last argument k is associated with b. Writing the smoothing kernels explicitly, we have PD[ R a ], R b (k) = WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)D(q, k q)B a a b (q, k q, k). (59) C. Matter-matter-matter cross-spectra For cross-spectra of smoothed dark matter fields (a = b = m), the DM bispectrum (16) gives PD[ R m], R m (k) = Z d3 q (2⇡)3 D(q, k q)B R m R m R m (q, k q, k) (60) = 2IR DF2 (k) + 4Ibare,R DF2 (k), (61) where we used that the kernel D(q, k q) is assumed to be symmetric in its arguments and we defined for kernels D and E IR DE(k) ⌘ WR(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)Plin mm(|k q|)D(q, k q)E(q, k q), (62) which is symmetric under D $ E, and Ibare,R DE (k) ⌘ WR(k)Plin mm(k) Z d3 q (2⇡)3 WR(q)WR(|k q|)Plin mm(q)D(q, k q)E(q, k), (63) which is not symmetric under D $ E. Explicit predictions for the cross-spectra P 2 m, m , P i m@i m, m and Ps2 m, m can be obtained from Eq. (61) by plugging in D = P0, D = F1 2 P1 and D = P2, respectively, and setting E = F2. Note that there are three powers of the smoothing kernel because we smooth the nonlinear rather than the linear field. The integrals in Eqs. (62) and (63) are similar to typical 1-loop expressions and can be reduced to two-dimensional integrals over scale q and cosine ˆq·ˆk, which can be evaluated numerically with little computational cost (see e.g. [43, 44] for public codes that compute similar integrals). The factors WR(|k q|) and Plin mm(|k q|) introduce a non-trivial angle dependence so that the angular integration generally needs to be performed numerically. The only ingredient for the theory prediction of Eq. (61) is the model for the DM bispectrum. Improved bispectrum Leading-order SPT for hhh cross-spectra! alo bispectrum (28) gives for the halo-halo-halo cross-spectra 3 1 h IR DF2 (k) + 2Ibare,R DF2 (k) i + 2b2 1b2 h IR DP0 (k) + 2Ibare,R DP0 (k) i + 4 3 b2 1bs2 h IR DP2 (k) + 2Ibare,R DP2 (k) i . (67) ending on b3 1 also appear in matter-matter-halo cross spectra, so that PD[ R h ], R h (k) b2 1PD[ R m], R h (k) = 4b2 1  b2Ibare,R DP0 (k) + 2 3 bs2 Ibare,R DP2 (k) . (68) F2 kernel in Legendre polynomials as in Eq. (18), Eq. (67) can be rewritten as PD[ R h ], R h (k) = ✓ 34 21 b3 1 + 2b2 1b2 ◆ h IR DP0 (k) + 2Ibare,R DP0 (k) i + 2b3 1 h IR D,F 1 2 P1 (k) + 2Ibare,R D,F 1 2 P1 (k) i + ✓ 8 21 b3 1 + 4 3 b2 1bs2 ◆ h IR DP2 (k) + 2Ibare,R DP2 (k) i . (69) ns to the theory expression of Eq. (67) are shown in Fig. 1 for Gaussian smoothing with R = g. 10 in the appendix for R = 10h 1 Mpc). Di↵erent colors describe di↵erent cross-spectra, P2}, while di↵erent line styles correspond to the contributions with di↵erent dependencies on
  • 37. SIMULATIONS RunPB sims from Martin White/Beth Reid! L = 1380 Mpc/h# 20483 DM particles or FoF (b=0.168) halos! 10 realizations! z = 0.55 Reid et al. 1404.3742! White et al. 1408.5435! White astro-ph/0207185
  • 38. DM CROSS-SPECTRA MS, Baldauf, Seljak, 1411.6595 squared density tidal shift
  • 39. DM CROSS-SPECTRA: COVARIANCE MS, Baldauf, Seljak, 1411.6595 squared density tidalshift Standard deviations of individual cross-spectra (sims divided by theory) Correlations between different cross-spectra at the same k theory (dashed) sims (solid) Perfectly (anti-)correlated at low k and high k, but uncorrelated at intermediate k
  • 40. HALO CROSS-SPECTRA squared density tidal shift MS, Baldauf, Seljak, 1411.6595 Assuming Poisson shot noise
  • 41. HALO CROSS-SPECTRA squared density tidal shift MS, Baldauf, Seljak, 1411.6595 Corrected shot noise (rescaling Poisson prediction by free Ashot)
  • 42. MASS DEPENDENCE OF SHOT NOISE CORRECTION 20 10 12 10 13 10 14 10 15 10 0 10 1 10 2 10 3 10 4 10 5 M [h -1 Msun ] ∆P[h-3 Mpc3 ] z=0 theo z=0.5 theo z=1.0 theo z=0 sim z=0.5 sim z=1.0 sim of the stochasticity correction on halo mass and redshift. The lines are based on linear bias parameters ound split and a fitting function modelling b2. The dashed lines and open points describe negative values. ow-k limits of the one and two bin splitting as the diamond and squares, respectively. The dotted line w mass correction arising from non-linear biasing whereas the dash-dotted blue, green and red lines show ss exclusion corrections for the three di↵erent redshifts. 1 Correction to 1/n shot noise in P(k) (Baldauf et al. 1305.2917) Correction to P(k)/n part of shot noise in bispectrum
  • 43. BIAS CONSTRAINTS 21 h 2 h| hi h i h i h| hi hs2 h| hi combined 0.5 0.0 0.5 1.0 1.5 b2 2 1 0 1 2 3 bs2 1.2 1.4 1.6 1.8 2.0 2.2 2.4 b1 0.4 0.2 0.0 0.2 0.4 As 0.5 0.0 0.5 1.0 1.5 b2 2 1 0 1 2 3 bs2 MS, Baldauf, Seljak, 1411.6595 squared density tidal shift Fit b1, b2, bs2, Ashot from hhh cross-spectra (only 3-point, no 2-point)
  • 44. BIAS CONSTRAINTS 21 h 2 h| hi h i h i h| hi hs2 h| hi combined 0.5 0.0 0.5 1.0 1.5 b2 2 1 0 1 2 3 bs2 1.2 1.4 1.6 1.8 2.0 2.2 2.4 b1 0.4 0.2 0.0 0.2 0.4 As 0.5 0.0 0.5 1.0 1.5 b2 2 1 0 1 2 3 bs2 MS, Baldauf, Seljak, 1411.6595 squared density tidal shift 
 l=1 shift contains almost entire b1 information Fit b1, b2, bs2, Ashot from hhh cross-spectra (only 3-point, no 2-point)
  • 46. COMPARISON WITH TRADITIONAL BISPECTRUM ESTIMATION Advantages of cross-spectra over estimating bispectrum for individual triangles! Simple# Nearly optimal (contains entire bispectrum information)! Fast (like power spectrum estimation)! Depends on just single k, i.e. covariances are easy! Challenges! Redshift space distortions: messier but doable (ideas/tricks welcome!)! Not optimal beyond tree level and beyond weakly non-Gaussian fields (could be improved with better theory or by combining with modal estimators that are sensitive to arbitrary bispectra) MS, Baldauf, Seljak, 1411.6595 Position-dependent power spectrum (Chiang++) is only sensitive to squeezed limit where grav. signal vanishes
  • 47. WHAT DO CMB PEOPLE DO? CMB bispectrum estimation! 2001: individual triangles of COBE data (lmax=20)! since 2003: KSW fNL estimators for separable bispectrum templates (now standard)! CMB lensing trispectrum:! ➟ never used individual quadrilaterals, but always max- likeli estimators for known trispectrum shape! ➟ reconstructed lensing potential is ! ➟ trispectrum = auto-power of this quadratic field! ISW-lensing bispectrum: cross-spectrum of and T! modal estimators (sensitive to arbitrary bispectra — reduce to our cross-spectra if halo bispectrum contributions are used as basis shapes) cr / TrT ˆ ⇠ T2 Komatsu et al. 2001 Komatsu+Spergel +Wandelt 2003! Lewis et al. 2011,
 Planck 2013 XIX (ISW) Fergusson+Shellard +Liguori 2009-2014,! Planck 2013 XXIV (NG)
  • 48. WHAT DO CMB PEOPLE DO? CMB bispectrum estimation! 2001: individual triangles of COBE data (lmax=20)! since 2003: KSW fNL estimators for separable bispectrum templates (now standard)! CMB lensing trispectrum:! ➟ never used individual quadrilaterals, but always max- likeli estimators for known trispectrum shape! ➟ reconstructed lensing potential is ! ➟ trispectrum = auto-power of this quadratic field! ISW-lensing bispectrum: cross-spectrum of and T! modal estimators (sensitive to arbitrary bispectra — reduce to our cross-spectra if halo bispectrum contributions are used as basis shapes) cr / TrT ˆ ⇠ T2 Komatsu et al. 2001 Komatsu+Spergel +Wandelt 2003! Lewis et al. 2011,
 Planck 2013 XIX (ISW) Fergusson+Shellard +Liguori 2009-2014,! Planck 2013 XXIV (NG) 
 LSS methods at the level where CMB was 2001
  • 49. WHAT DO CMB PEOPLE DO? CMB bispectrum estimation! 2001: individual triangles of COBE data (lmax=20)! since 2003: KSW fNL estimators for separable bispectrum templates (now standard)! CMB lensing trispectrum:! ➟ never used individual quadrilaterals, but always max- likeli estimators for known trispectrum shape! ➟ reconstructed lensing potential is ! ➟ trispectrum = auto-power of this quadratic field! ISW-lensing bispectrum: cross-spectrum of and T! modal estimators (sensitive to arbitrary bispectra — reduce to our cross-spectra if halo bispectrum contributions are used as basis shapes) cr / TrT ˆ ⇠ T2 Komatsu et al. 2001 Komatsu+Spergel +Wandelt 2003! Lewis et al. 2011,
 Planck 2013 XIX (ISW) Fergusson+Shellard +Liguori 2009-2014,! Planck 2013 XXIV (NG) 
 LSS methods at the level where CMB was 2001 
 Cross-spectrum method is first step to modernize this for LSS! 
 but it’s really only a first step: 
 our method is not yet ready for real data, 
 while traditional method has been applied to real data (most recently by Gil-Marin et al. 2014)
  • 50. Explored new estimators to measure bispectrum parameters in a nearly optimal way using cross-spectra of 3 quadratic fields with the density! ! ! ! ! Simulations agree with theory on large scales! Linear halo bias b1 mostly determined by shift term cross density! Shot noise requires non-Poissonian corrections ! Future: e.g. RSDs, model to higher k, apply to real data CONCLUSIONS (PART I) MS, Baldauf, Seljak, 1411.6595 te the density gradient @i (x), the displacement field i(x) = @i@ 2 (x), and the tidal tensor sij(x 2 1 3 (K) ij ⇤ (x). te the squared density 2 (x), the shift term i (x)@i (x) and the tidal term s2 (x) = 3 2 sij(x)sij(x). transform the three quadratic fields to get [ 2 ](k), [ i @i ](k) and [s2 ](k). te the cross-spectra between the quadratic fields and the density, i.e. (suppressing division by the num es) ˆP 2, (k) ⇠ X k,|k|=k [ 2 ](k) ( k), ( ˆP i@i , (k) ⇠ X k,|k|=k [ i @i ](k) ( k) ( ˆPs2, (k) ⇠ X k,|k|=k [s2 ](k) ( k). ( ed from the Legendre decomposition of the halo bispectrum, the l = 1 shift cross-spectrum (88) conta ntire bispectrum information on the linear bias b1, while the l = 0 squared density cross-spectrum ( 2 tidal cross-spectrum (89) mostly improve constraints on b2 and bs2 once b1 is known. Measuring pectra and comparing them to their theory predictions is equivalent to an optimal maximum-likelih the amplitudes of contributions to dark matter or halo bispectra (under certain regularity conditions; Therefore, these cross-spectra contain the same constraining power on bias parameters and 8 as a ectrum analysis. Measuring cross-spectra is both simpler and computationally cheaper than perform trum measurements for individual triangle configurations. Since they only depend on a single rather t umbers, modeling the covariance is also simpler. derived leading-order perturbation theory predictions for the expectation values and covariances of pectra, where both the quadratic and the single field can be dark matter or halo fields, and sec
  • 51. PART II! 
 EULERIAN BAO RECONSTRUCTIONS 
 & 
 N-POINT STATISTICS! 
 
 ARXIV:1508.06972! W/ Y. FENG, F. BEUTLER, B. SHERWIN, M.Y. CHU
  • 52. BARYONIC ACOUSTIC OSCILLATIONS (BAO) Eisenstein/Seo/White 2006! – 5 – Coupled baryon- photon fluid
  • 53. BARYONIC ACOUSTIC OSCILLATIONS (BAO) Eisenstein/Seo/White 2006! – 5 – Coupled baryon- photon fluid – 5 – Expansion
  • 54. BARYONIC ACOUSTIC OSCILLATIONS (BAO) Eisenstein/Seo/White 2006! – 5 – Coupled baryon- photon fluid – 5 – Expansion – 5 – Decoupling Photons free-stream
  • 55. BARYONIC ACOUSTIC OSCILLATIONS (BAO) Eisenstein/Seo/White 2006! – 5 – Coupled baryon- photon fluid – 5 – Expansion – 5 – Decoupling Photons free-stream – 5 – Baryons cluster in shell Expansion BAO scale r=150Mpc
  • 56. Eisenstein/Seo/White 2006! s profile versus comoving radius of an initially point-like BAO scale r=150Mpc The As the perturbation g equilibrium The final configuration put in by hand) and an “ Further (non-linear) proce slightly shift the peak -- b Expansion + clustering BARYONIC ACOUSTIC OSCILLATIONS (BAO)
  • 57. BAO IMPRINT ON LSS Correlation function : BAO bump Power spectrum P(k): BAO wiggles⇠(r) x 0.7!
  • 58. BAO IMPRINT ON LSS Non-linearities: ! Add small-scale power! Smear out BAO peak, i.e. degrade BAO information! Slightly shift BAO scale! Correlation function : BAO bump Power spectrum P(k): BAO wiggles⇠(r) x 0.7!
  • 59. BAO RECONSTRUCTION Goal: Restore linear modes to increase BAO information! ! Method: Undo large-scale flows that broaden the BAO peak:! Calculate large-scale displacement/velocity field with Zeldovich approximation! ! ! Displace clustered and random catalog by this! ➡ get `displaced’ and `shifted’ densities δd and δs! Reconstructed density = δd - δs s(k) = ik k2 WR(k) (k) power spectrum after reconstruction by Fig. 3.— The real-space matter correlation function after recon struction by the linear-theory density-velocity relation, with th density field Gaussian filtered. The black solid line shows the co relation function at z = 49. The blue short-dashed line shows at z = 0.3; the acoustic peak has been smeared out. The re dot-dashed and magenta long-dashed lines show the effects of r construction for 20h−1 Mpc and 10h−1 Mpc Gaussian filtering, r no reconstruction linear reconstructed Simulations Eisenstein et al. 2006! Padmanabhan et al. 2008
  • 60. BAO RECONSTRUCTION Works great in practice: 
 For BOSS DR11, BAO signal-to-noise improved by ~50%, 
 achieving sub-percent BAO scale constraint Padmanabhan et al. 2012! Anderson et al. 2013! BAO in SDSS-III BOSS galaxies 17 Figure 11. DR11 CMASS clustering measurements (black circles) with ⇠(s) shown in the left panels and P(k) in the right panels. The top panels show the Anderson et al. 2013! (BOSS DR11)!
  • 62. NEW ALGORITHMS: WHY? Motivations to dig deeper & explore new reconstruction algorithms:! Where does the new BAO information come from?! Can we derive reconstruction algorithm(s) better?! Can we do better in terms of BAO information?! Can we reduce intermixing of model and data when reconstructing?! (e.g. standard reconstruction makes assumptions on f, galaxy bias, gravity when transforming data)! ! Tried to address some of these points in recent paper (1508.06972)
  • 63. NEW ALGORITHMS: CATEGORIES Useful to consider two categories of reconstruction algorithms: Eulerian reconstructions Never displace any objects! Work only with Eulerian density! E.g. rec = 2
  • 64. NEW ALGORITHMS: CATEGORIES Useful to consider two categories of reconstruction algorithms: Lagrangian reconstructions Displace objects at some stage of the algorithm! E.g. standard algorithm (Eisenstein et al. 2006) Eulerian reconstructions Never displace any objects! Work only with Eulerian density! E.g. rec = 2
  • 65. NEW EULERIAN ALGORITHM I Eulerian growth-shift (EGS) reconstruction! Go back in time MS et al. 1508.06972! rec(x) = (x, ⌘ ⌘) ⇡ (x, ⌘) ⌘ @⌘ (x, ⌘)
  • 66. NEW EULERIAN ALGORITHM I Eulerian growth-shift (EGS) reconstruction! Go back in time MS et al. 1508.06972! rec(x) = (x, ⌘ ⌘) ⇡ (x, ⌘) ⌘ @⌘ (x, ⌘) @⌘ + r · [(1 + )v] = 0 ) @⌘ = r · v v · r r · v Get density time derivative nonperturbatively from nonlinear continuity equation
  • 67. NEW EULERIAN ALGORITHM I Eulerian growth-shift (EGS) reconstruction! Go back in time MS et al. 1508.06972! Approximate velocity in terms of smoothed density δR (using linear relation)! ) r · v = Rv(k) ⇡ s(k) ⌘ ik k2 R(k) - Not reverting linear time evolution! - Not displacing any objects, i.e. Eulerian! - Nonperturbative in δ, linear in velocity ) rec EGS(x) = (x) s(x) · r (x) | {z } shift (x) R(x) | {z } growth rec(x) = (x, ⌘ ⌘) ⇡ (x, ⌘) ⌘ @⌘ (x, ⌘) @⌘ + r · [(1 + )v] = 0 ) @⌘ = r · v v · r r · v Get density time derivative nonperturbatively from nonlinear continuity equation
  • 68. NEW EULERIAN ALGORITHM I Eulerian growth-shift (EGS) reconstruction! Reconstructed power spectrum:! MS et al. 1508.06972! 2 ⌦ 2 | ↵ 2 hs · r | i + ⌦ 2 | 2 ↵ + hs · r |s · r i + 2 ⌦ 2 |s · r ↵ 2-point 3-point 4-point ) h rec EGS| rec EGSi = h | i ➡ Automatically combines 2-, 3- and 4-point of the unreconstructed density! ➡ Explicitly see how reconstruction exploits 3- and 4-point BAO information! ➡ Cross-spectra are the same as in part I of the talk, i.e. nearly-optimal! ➡ Get similar combinations for all our other Eulerian reconstructions rec EGS = 2 s · r ) h rec EGS| rec EGSi = h | i
  • 69. NEW EULERIAN ALGORITHM II Eulerian F2 (EF2) reconstruction! Second-order density! ! ! Remove this from the full density MS et al. 1508.06972! (2) (x) = 17 21 2 0(x) | {z } growth 0(x) · r 0(x) | {z } shift + 4 21 K2 0 (x) | {z } tidal rec EF2(x) ⌘ (x) 17 21 R(x) (x) | {z } growth s(x) · r (x) | {z } shift 4 21 K2 R(x) | {z } tidal More formal derivation using Newton-Raphson iteration: 
 Find linear density compatible with a given observed nonlinear density, i.e. solve
 for δ0!f[ 0] ⌘ 0 + F2[ 0] obs = 0
  • 70. NEW LAGRANGIAN ALGORITHMS Lagrangian Random-random (LRR) reconstruction! 2LPT theory for clustered and random catalogs displaced by negative or positive Zeldovich displacement: δd[s], δd[-s], δs[s], δs[-s] ! Only 2 of 6 possible combinations suppress 2nd order nonlinearities:! Lagrangian growth-shift (LGS) reconstruction (= standard algorithm)! ! Lagrangian random-random (LRR) reconstruction:! ! ! MS et al. 1508.06972! rec LGS = d[s] s[s] rec LRR = 1 2 ⇢ s[s] + s[ s]
  • 71. NEW LAGRANGIAN ALGORITHMS Lagrangian F2 (LF2) reconstruction! Unique combination of LRR and standard LGS algorithms reverses full second-order density (up to smoothing)! MS et al. 1508.06972! rec LF2 = 3 7 rec LGS + 4 7 rec LRR
  • 72. OVERVIEW All reconstruction algorithms MS et al. 1508.06972! Eulerian Lagrangian Growth-Shift rec EGS(x) = (x) R(x) (x) s(x) · r (x) rec LGS = d[s] Full F2 rec EF2(x) = (x) 17 21 R(x) (x) s(x)r (x) 4 21 K2 R(x) rec LF2 = 3 7 rec LGS Random-Random rec ERR = (x) 2 3 2 R(x) s(x) · r R(x) 1 3 K2 RR(x) rec LRR = 1 2 TABLE I. Overview of the reconstruction algorithms considered in this paper. Lagrangian reconstructions involv of objects which is not the case for Eulerian reconstructions. Methods in the same row agree at second order LPT BAO reconstruction algorithm is called Lagrangian growth-shift reconstruction and listed on the upper right. 1. LGS: Lagrangian Growth-Shift Reconstruction (Section IV B) This is the standard BAO reconstruction algorithm proposed in [8]: Objects in clustered and rando displaced by the large-scale negative Zeldovich displacement field s, and the di↵erence between the ‘displaced’ and ‘shifted ’densities d[s] and s[s] gives the reconstructed density, rec LGS = d[s] s[s]. Eulerian Lagrangian wth-Shift rec EGS(x) = (x) R(x) (x) s(x) · r (x) rec LGS = d[s] s[s] F2 rec EF2(x) = (x) 17 21 R(x) (x) s(x)r (x) 4 21 K2 R(x) rec LF2 = 3 7 rec LGS + 4 7 rec LRR dom-Random rec ERR = (x) 2 3 2 R(x) s(x) · r R(x) 1 3 K2 RR(x) rec LRR = 1 2 { s[s] + s LE I. Overview of the reconstruction algorithms considered in this paper. Lagrangian reconstructions involve displace jects which is not the case for Eulerian reconstructions. Methods in the same row agree at second order LPT. The sta O reconstruction algorithm is called Lagrangian growth-shift reconstruction and listed on the upper right. . LGS: Lagrangian Growth-Shift Reconstruction (Section IV B) This is the standard BAO reconstruction algorithm proposed in [8]: Objects in clustered and random catalog displaced by the large-scale negative Zeldovich displacement field s, and the di↵erence between the correspon ‘displaced’ and ‘shifted ’densities d[s] and s[s] gives the reconstructed density, rec LGS = d[s] s[s]. 11 Eulerian Lagrangian rec EGS(x) = (x) R(x) (x) s(x) · r (x) rec LGS = d[s] s[s] rec EF2(x) = (x) 17 21 R(x) (x) s(x)r (x) 4 21 K2 R(x) rec LF2 = 3 7 rec LGS + 4 7 rec LRR om rec ERR = (x) 2 3 2 R(x) s(x) · r R(x) 1 3 K2 RR(x) rec LRR = 1 2 { s[s] + s[ s]} rview of the reconstruction algorithms considered in this paper. Lagrangian reconstructions involve displacements h is not the case for Eulerian reconstructions. Methods in the same row agree at second order LPT. The standard uction algorithm is called Lagrangian growth-shift reconstruction and listed on the upper right. agrangian Growth-Shift Reconstruction (Section IV B) he standard BAO reconstruction algorithm proposed in [8]: Objects in clustered and random catalogs are d by the large-scale negative Zeldovich displacement field s, and the di↵erence between the corresponding ed’ and ‘shifted ’densities d[s] and s[s] gives the reconstructed density, rec LGS = d[s] s[s]. (57) Eulerian Lagrangian Equivalent in 2LPT* * If displaced clustered catalogs are modelled such that displacement field is evaluated at Eulerian, not Lagrangian position, i.e. s[x] instead of s[q].
  • 74. SIMULATIONS FastPM code by Yu Feng! Particle-mesh code, time-stepping linear in scale factor a, parallel COLA! Ran wiggle and nowiggle simulations with same initial phases! 20483 DM particles, L=1380Mpc/h, 80 timesteps, 3 realizations, take 1% subsample at z=0.55; use R=15Mpc/h smoothing # ! RunPB simulations by Martin White! Full TreePM (more accurate and expensive)! Only wiggle simulations!
  • 75. OVERVIEW All reconstruction algorithms MS et al. 1508.06972! Eulerian Lagrangian Growth-Shift rec EGS(x) = (x) R(x) (x) s(x) · r (x) rec LGS = d[s] Full F2 rec EF2(x) = (x) 17 21 R(x) (x) s(x)r (x) 4 21 K2 R(x) rec LF2 = 3 7 rec LGS Random-Random rec ERR = (x) 2 3 2 R(x) s(x) · r R(x) 1 3 K2 RR(x) rec LRR = 1 2 TABLE I. Overview of the reconstruction algorithms considered in this paper. Lagrangian reconstructions involv of objects which is not the case for Eulerian reconstructions. Methods in the same row agree at second order LPT BAO reconstruction algorithm is called Lagrangian growth-shift reconstruction and listed on the upper right. 1. LGS: Lagrangian Growth-Shift Reconstruction (Section IV B) This is the standard BAO reconstruction algorithm proposed in [8]: Objects in clustered and rando displaced by the large-scale negative Zeldovich displacement field s, and the di↵erence between the ‘displaced’ and ‘shifted ’densities d[s] and s[s] gives the reconstructed density, rec LGS = d[s] s[s]. Eulerian Lagrangian wth-Shift rec EGS(x) = (x) R(x) (x) s(x) · r (x) rec LGS = d[s] s[s] F2 rec EF2(x) = (x) 17 21 R(x) (x) s(x)r (x) 4 21 K2 R(x) rec LF2 = 3 7 rec LGS + 4 7 rec LRR dom-Random rec ERR = (x) 2 3 2 R(x) s(x) · r R(x) 1 3 K2 RR(x) rec LRR = 1 2 { s[s] + s LE I. Overview of the reconstruction algorithms considered in this paper. Lagrangian reconstructions involve displace jects which is not the case for Eulerian reconstructions. Methods in the same row agree at second order LPT. The sta O reconstruction algorithm is called Lagrangian growth-shift reconstruction and listed on the upper right. . LGS: Lagrangian Growth-Shift Reconstruction (Section IV B) This is the standard BAO reconstruction algorithm proposed in [8]: Objects in clustered and random catalog displaced by the large-scale negative Zeldovich displacement field s, and the di↵erence between the correspon ‘displaced’ and ‘shifted ’densities d[s] and s[s] gives the reconstructed density, rec LGS = d[s] s[s]. 11 Eulerian Lagrangian rec EGS(x) = (x) R(x) (x) s(x) · r (x) rec LGS = d[s] s[s] rec EF2(x) = (x) 17 21 R(x) (x) s(x)r (x) 4 21 K2 R(x) rec LF2 = 3 7 rec LGS + 4 7 rec LRR om rec ERR = (x) 2 3 2 R(x) s(x) · r R(x) 1 3 K2 RR(x) rec LRR = 1 2 { s[s] + s[ s]} rview of the reconstruction algorithms considered in this paper. Lagrangian reconstructions involve displacements h is not the case for Eulerian reconstructions. Methods in the same row agree at second order LPT. The standard uction algorithm is called Lagrangian growth-shift reconstruction and listed on the upper right. agrangian Growth-Shift Reconstruction (Section IV B) he standard BAO reconstruction algorithm proposed in [8]: Objects in clustered and random catalogs are d by the large-scale negative Zeldovich displacement field s, and the di↵erence between the corresponding ed’ and ‘shifted ’densities d[s] and s[s] gives the reconstructed density, rec LGS = d[s] s[s]. (57) Eulerian Lagrangian Equivalent in 2LPT* * If displaced clustered catalogs are modelled such that displacement field is evaluated at Eulerian, not Lagrangian position, i.e. s[x] instead of s[q].
  • 76. OVERVIEW All reconstruction algorithms MS et al. 1508.06972! Eulerian Lagrangian Growth-Shift rec EGS(x) = (x) R(x) (x) s(x) · r (x) rec LGS = d[s] Full F2 rec EF2(x) = (x) 17 21 R(x) (x) s(x)r (x) 4 21 K2 R(x) rec LF2 = 3 7 rec LGS Random-Random rec ERR = (x) 2 3 2 R(x) s(x) · r R(x) 1 3 K2 RR(x) rec LRR = 1 2 TABLE I. Overview of the reconstruction algorithms considered in this paper. Lagrangian reconstructions involv of objects which is not the case for Eulerian reconstructions. Methods in the same row agree at second order LPT BAO reconstruction algorithm is called Lagrangian growth-shift reconstruction and listed on the upper right. 1. LGS: Lagrangian Growth-Shift Reconstruction (Section IV B) This is the standard BAO reconstruction algorithm proposed in [8]: Objects in clustered and rando displaced by the large-scale negative Zeldovich displacement field s, and the di↵erence between the ‘displaced’ and ‘shifted ’densities d[s] and s[s] gives the reconstructed density, rec LGS = d[s] s[s]. Eulerian Lagrangian wth-Shift rec EGS(x) = (x) R(x) (x) s(x) · r (x) rec LGS = d[s] s[s] F2 rec EF2(x) = (x) 17 21 R(x) (x) s(x)r (x) 4 21 K2 R(x) rec LF2 = 3 7 rec LGS + 4 7 rec LRR dom-Random rec ERR = (x) 2 3 2 R(x) s(x) · r R(x) 1 3 K2 RR(x) rec LRR = 1 2 { s[s] + s LE I. Overview of the reconstruction algorithms considered in this paper. Lagrangian reconstructions involve displace jects which is not the case for Eulerian reconstructions. Methods in the same row agree at second order LPT. The sta O reconstruction algorithm is called Lagrangian growth-shift reconstruction and listed on the upper right. . LGS: Lagrangian Growth-Shift Reconstruction (Section IV B) This is the standard BAO reconstruction algorithm proposed in [8]: Objects in clustered and random catalog displaced by the large-scale negative Zeldovich displacement field s, and the di↵erence between the correspon ‘displaced’ and ‘shifted ’densities d[s] and s[s] gives the reconstructed density, rec LGS = d[s] s[s]. 11 Eulerian Lagrangian rec EGS(x) = (x) R(x) (x) s(x) · r (x) rec LGS = d[s] s[s] rec EF2(x) = (x) 17 21 R(x) (x) s(x)r (x) 4 21 K2 R(x) rec LF2 = 3 7 rec LGS + 4 7 rec LRR om rec ERR = (x) 2 3 2 R(x) s(x) · r R(x) 1 3 K2 RR(x) rec LRR = 1 2 { s[s] + s[ s]} rview of the reconstruction algorithms considered in this paper. Lagrangian reconstructions involve displacements h is not the case for Eulerian reconstructions. Methods in the same row agree at second order LPT. The standard uction algorithm is called Lagrangian growth-shift reconstruction and listed on the upper right. agrangian Growth-Shift Reconstruction (Section IV B) he standard BAO reconstruction algorithm proposed in [8]: Objects in clustered and random catalogs are d by the large-scale negative Zeldovich displacement field s, and the di↵erence between the corresponding ed’ and ‘shifted ’densities d[s] and s[s] gives the reconstructed density, rec LGS = d[s] s[s]. (57) Eulerian Lagrangian Equivalent in 2LPT* * If displaced clustered catalogs are modelled such that displacement field is evaluated at Eulerian, not Lagrangian position, i.e. s[x] instead of s[q].
  • 78. GROWTH-SHIFT ALGORITHMS 2D slice plots
 Density change due to reconstruction, δ - δrec MS et al. 1508.06972! Eulerian growth-shift (EGS) Lagrangian growth-shift (LGS)
  • 79. GROWTH-SHIFT ALGORITHMS BAO signal: Fractional difference of power spectra between wiggle and nowiggle simulations, for Lagrangian and Eulerian growth-shift algorithms! MS et al. 1508.06972! No reconstruction Perfect reconstruction Eulerian growth-shift Lagrangian growth-shift
  • 80. GROWTH-SHIFT ALGORITHMS Cumulative BAO signal-to-noise-squared:
 Eulerian algorithm yields 95% of BAO S/N of standard Lagrangian algorithm MS et al. 1508.06972! No reconstruction Perfect reconstruction Eulerian growth-shift Lagrangian growth-shift
  • 81. WHERE DOES! NEW BAO INFO! COME FROM?
  • 82. GROWTH-SHIFT ALGORITHMS 3-point vs 4-point: Split δrec = δ + (δrec-δ) in simulations MS et al. 1508.06972! ) h rec | rec i = h | i |{z} 2pt + 2 h( rec )| i | {z } 3pt + h( rec )|( rec )i | {z } 4pt .
  • 83. GROWTH-SHIFT ALGORITHMS 3-point vs 4-point: Split δrec = δ + (δrec-δ) in simulations MS et al. 1508.06972! ) h rec | rec i = h | i |{z} 2pt + 2 h( rec )| i | {z } 3pt + h( rec )|( rec )i | {z } 4pt . ➡ 3-point much more important than 4-point
  • 84. GROWTH-SHIFT ALGORITHMS 13 vs 22 part (of 3-point): Split δ = δ0 + (δ-δ0) in simulations, where δ0 = linear density MS et al. 1508.06972! ) h( rec )| i = h( rec )| 0i | {z } ⇠h (3) 0i + h( rec )|( 0)i | {z } ⇠h (2) (2) i
  • 85. GROWTH-SHIFT ALGORITHMS 13 vs 22 part (of 3-point): Split δ = δ0 + (δ-δ0) in simulations, where δ0 = linear density MS et al. 1508.06972! ➡ 13 part of 3-point sharpens BAO peaks,
 22-part shifts and damps them 
 (see also Padmanabhan et al 2009) ) h( rec )| i = h( rec )| 0i | {z } ⇠h (3) 0i + h( rec )|( 0)i | {z } ⇠h (2) (2) i
  • 86. GROWTH-SHIFT ALGORITHMS Growth vs shift part MS et al. 1508.06972!
  • 87. GROWTH-SHIFT ALGORITHMS Growth vs shift part MS et al. 1508.06972! ➡ Shift part most important, 
 but growth also crucial
  • 89. ALL ALGORITHMS Performance comparison! EGS algorithm yields 95% of BAO S/N of standard Lagrangian LGS algorithm! EF2 algorithm similar, other algorithms significantly worse MS et al. 1508.06972!
  • 90. DISCUSSION Pros & Cons MS et al. 1508.06972! Lagrangian reconstructions - Less transparent where BAO info comes from! - Data transformation very dependent on fiducial model! - Justification of algorithm mostly a posteriori
 ! +Slightly better performance! +Well established (10 yr old), applied to observations Eulerian reconstructions +BAO info comes from specific 3- & 4-point! +Data transformations less dependent on fiducial model! +Derived from nonperturbative continuity equation (EGS) or Newton-Raphson (EF2)! - Slightly worse performance! - Not worked out in redshift space
  • 91. DISCUSSION Pros & Cons MS et al. 1508.06972! Lagrangian reconstructions - Less transparent where BAO info comes from! - Data transformation very dependent on fiducial model! - Justification of algorithm mostly a posteriori
 ! +Slightly better performance! +Well established (10 yr old), applied to observations Eulerian reconstructions +BAO info comes from specific 3- & 4-point! +Data transformations less dependent on fiducial model! +Derived from nonperturbative continuity equation (EGS) or Newton-Raphson (EF2)! - Slightly worse performance! - Not worked out in redshift space Equivalent in 2LPT! ! ➡ Reconstructions are connected! ➡ New argument for success and robustness of standard reconstruction: implicitly includes 3- and 4-point! ➡ Intuitively, expect little new BAO information in 3-point statistics, because rec. moves it into 2-point
  • 93. CONCLUSIONS Simple bispectrum estimators! Three cross-spectra of quadratic fields with the density measure projection of full bispectrum on tree-level shape! Simple, fast, nearly-optimal, simpler covariances! Works well in simulations! Extension to redshift space under development! ! Eulerian BAO reconstruction and higher N-point statistics! Presented 5 new BAO reconstruction algorithms! Showed connection to 3- & 4-point of unreconstructed density! Connected various algorithms to each other via 2LPT modelling! Standard algorithm performs best, but two Eulerian algorithms are almost as well, achieving ~95% BAO S/N of standard method! MS et al. 1508.06972! MS, Baldauf, Seljak 1411.6595!
  • 96. 11 FIG. 1. Theory contributions (67) to halo-halo-halo cross-spectra scaling like b3 1 (dashed), b2 1b2 (dash-dotted) and b2 1bs2 (dotted) for squared density 2 h(x) (blue), shift term i h(x)@i h(x) (red) and tidal term s2 h(x) (green), evaluated for fixed bias parameters b1 = 1, b2 = 0.5 and bs2 = 2, Gaussian smoothing with RG = 20h 1 Mpc, at z = 0.55, with linear matter power spectra in integrands. Thin gray lines show the large-scale (low k) limit given by Eq. (70). The cross-spectra are divided by the partially smoothed FrankenEmu emulator matter power spectrum W 3/2 R Pemu mm [45–48] for plotting convenience. THEORY CONTRIBUTIONS
  • 97. VERY LARGE SCALES Large scale limit (k→0) of cross-spectrum expectation values: MS, Baldauf, Seljak, 1411.6595 0 IR DE(k) = WR(k)⌧4 R, D 2 P0, F1 2 P1, P2 , E 2 P0, P2 , (A15) ⌧4 R ⌘ 1 2⇡2 Z dq q2 W2 R(q)P2 mm(q). (A16) tegrals (63) for E = F2 follow from Eqs. (A10) and (A12) by noting that only P2 0(⌫) tegration over the product of Eqs. (A10) and (A12) because of Legendre polynomial G (k) = 1 4 WR(k)Pmm(k) ✓ 68 21 2 R 2 3 2 R,W 0 R ◆ , D 2 P0, F1 2 P1, P2 , (A17) 2 R ⌘ 1 2⇡2 Z dq q2 W2 R(q)Pmm(q) (A18) 2 R,W 0 R ⌘ 1 2⇡2 Z dq q2 d ln WR(q) d ln q W2 R(q)Pmm(q). (A19) d ln WR(q)/d ln q = (qR)2 , so no numerical derivatives are required to evaluate Eq. (A19). can be integrated by parts to get 2 R,W 0 R = 1 2 2 R,P 0 , where 2 R,P 0 ⌘ 1 2⇡2 Z dq q2 W2 R(q)Pmm(q) d ln q3 Pmm(q) d ln q . (A20) r D 2 {P0, F1 2 P1, P2} k) = 1 4 WR(k)Pmm(k) ✓ 68 21 2 R 1 3 2 R,P 0 ◆ (A21) = 1 4 WR(k)Pmm(k) 1 2⇡2 Z dq q2 W2 R(q)Pmm(q) ✓ 68 21 1 3 d ln q3 Pmm(q) d ln q ◆ . (A22) ⌧4 R ⌘ 1 2⇡2 dq q2 W2 R(q)P2 mm(q). (A16) e bare integrals (63) for E = F2 follow from Eqs. (A10) and (A12) by noting that only P2 0(⌫) the ⌫ integration over the product of Eqs. (A10) and (A12) because of Legendre polynomial m !0 Ibare,RG DF2 (k) = 1 4 WR(k)Pmm(k) ✓ 68 21 2 R 2 3 2 R,W 0 R ◆ , D 2 P0, F1 2 P1, P2 , (A17) 2 R ⌘ 1 2⇡2 Z dq q2 W2 R(q)Pmm(q) (A18) 2 R,W 0 R ⌘ 1 2⇡2 Z dq q2 d ln WR(q) d ln q W2 R(q)Pmm(q). (A19) thing, d ln WR(q)/d ln q = (qR)2 , so no numerical derivatives are required to evaluate Eq. (A19). Eq. (A19) can be integrated by parts to get 2 R,W 0 R = 1 2 2 R,P 0 , where 2 R,P 0 ⌘ 1 2⇡2 Z dq q2 W2 R(q)Pmm(q) d ln q3 Pmm(q) d ln q . (A20) ecomes for D 2 {P0, F1 2 P1, P2} Ibare,RG DF2 (k) = 1 4 WR(k)Pmm(k) ✓ 68 21 2 R 1 3 2 R,P 0 ◆ (A21) = 1 4 WR(k)Pmm(k) 1 2⇡2 Z dq q2 W2 R(q)Pmm(q) ✓ 68 21 1 3 d ln q3 Pmm(q) d ln q ◆ . (A22) tains the linear response of the power spectrum to a long-wavelength overdensity (e.g. [17, 33, 58]). rge-scale limit of the quantity proposed in [17] is (by construction) proportional to the response cales rather than integrating over it. y the P0(⌫) part of Eq. (A12) survives the ⌫-integration so that lim k!0 Ibare,R DP0 (k) = WR(k)Pmm(k) 2 R, D 2 P0, F1 2 P1, P2 . (A23) 24 WR(|k q|) = WR(q)  1 k⌫ q d ln WR(q) d ln q . (A12) s becomes WRG (|k q|) = WRG (q)  1 + k⌫ q (qR)2 . (A13) der (k/q)0 for the final results from now on we get from Eqs. (A7), (A8), (A11) and (A12) lim k!0 IR DF2 (k) = 0, D 2 P0, F1 2 P1, P2 . (A14) m 0 IR DE(k) = WR(k)⌧4 R, D 2 P0, F1 2 P1, P2 , E 2 P0, P2 , (A15) ⌧4 R ⌘ 1 2⇡2 Z dq q2 W2 R(q)P2 mm(q). (A16) ntegrals (63) for E = F2 follow from Eqs. (A10) and (A12) by noting that only P2 0(⌫) ntegration over the product of Eqs. (A10) and (A12) because of Legendre polynomial G (k) = 1 4 WR(k)Pmm(k) ✓ 68 21 2 R 2 3 2 R,W 0 R ◆ , D 2 P0, F1 2 P1, P2 , (A17) 1 Z for D 2 {P0, F1 2 P1, P2} with hD[ R a ](k) R b For a 6= b, the bispectrum B Writing the smoothing kernels PD[ R a ], R b (k) = For cross-spectra of smooth 1 1 2 1 s exploited to fit b1, b2 and bs2 to the three cross-spectra at the same time. In practice, the fitted bias pa still be degenerate because sampling variance at low k and modeling uncertainty at high k limit the us In particular, in the range 0.01h/Mpc . k . 0.1h/Mpc, every cross-spectrum depends rather similarly o leading to a degeneracy where larger b1 can be compensated by a smaller b2, which is also present when individual bispectrum triangles rather than cross-spectra. Consequently, models that extend leading- higher k are expected to improve bias constraints significantly. In the large-scale limit, k ⌧ q, the three halo-halo-halo cross-spectra of Eq. (67) equal each other (see for details), lim k!0 PD[ R h ], R h (k) = WR(k)  b3 1Plin mm(k) ✓ 68 21 2 R 1 3 2 R,P 0 ◆ + 2b2 1b2 ⌧4 R + 2Plin mm(k) 2 R + 4 3 b2 1bs2 ⌧4 R , D 2 P0, F1 2 P where WR(k) ! 1 for k ! 0, and 2 R,P 0 and ⌧4 R are defined in Eqs. (A20) and (A16), respectively. the b3 1 term is proportional to the linear matter power spectrum, whereas the term involving b2 scales li matter power spectrum plus a k-independent correction and the term involving bs2 is entirely k-inde thin gray lines in Figs. 1 and 10 for the the ratio of these limits over the matter power spectrum). sampling variance at low k, this scale-dependence is expected to be less powerful in distinguishing bia than constraints obtained from the di↵erent scale-dependencies at high k. F. Shot noise The bispectrum of the smoothed halo density has an additional stochasticity contribution, h ˆBR hhh(k1, k2, k3)i = BR hhh(k1, k2, k3) + BR,shot hhh (k1, k2, k3), whose Poissonian prediction is (see e.g. [49]) BR,shot hhh (k1, k2, k3) = WR(k1)WR(k2)WR(k3) ⇢ 1 ¯nh [Phh(k1) + 2 perms] + 1 ¯n2 h . Different from position-dependent power spectrum (Chiang et al. 2014): where q1,3 refers to q1 and q3. Spherically averaging (ˆk · ˆq3)2 over k yields 1/3, and thus d2Ωˆk 4π B(k − q1, −k + q1 + q3, −q3) = 47 21 − 1 3 d ln P(k) d ln k P(k)P(q3) + O q1,3 k 2 = 68 21 − 1 3 d ln k3P(k) d ln k P(k)P(q3) + O q1,3 k 2 . (A.9) where q1,3 refers to q1 and q3. Spherically averaging (ˆk · ˆq3)2 over k yields 1/3, and thus d2Ωˆk 4π B(k − q1, −k + q1 + q3, −q3) = 47 21 − 1 3 d ln P(k) d ln k P(k)P(q3) + O q1,3 k 2 = 68 21 − 1 3 d ln k3P(k) d ln k P(k)P(q3) + O q1,3 k 2 . lim k!0
  • 99. GROWTH-SHIFT ALGORITHMS 2D slice plots: Components of EGS reconstruction MS et al. 1508.06972! rec EGS(x) = (x) (x) R(x) | {z } growth s(x) · r (x) | {z } shift random (middle) and F2 (right) algorithms. The panels show 2D slices of excess densities r density when performing reconstruction. The excess densities in the bottom panels are quadr measured from simulations. They coincide with the excess densities expected from modeling th n the upper panels with 2LPT, if the linear densities in Eqs. (46), (48) and (53) are replaced by and ERR, twice the excess density is shown to enhance color contrast. We use R = 15h 1 Mpc reconstructions. To highlight large scales, each final field is additionally smoothed externally wit The clustered catalog is obtained from a 1% random subsample of one RunPB N-body realizat equally many randomly distributed particles. FIG. 11. Left panel: Nonlinear density before reconstruction (divided by 3 for better visibility parison of Lagrangian (top) and Eulerian (bottom) reconstructions at the field level for growth-shift (left), rand le) and F2 (right) algorithms. The panels show 2D slices of excess densities rec that are removed from performing reconstruction. The excess densities in the bottom panels are quadratic in the nonlinear den m simulations. They coincide with the excess densities expected from modeling the Lagrangian reconstruct anels with 2LPT, if the linear densities in Eqs. (46), (48) and (53) are replaced by the nonlinear ones. For L ce the excess density is shown to enhance color contrast. We use R = 15h 1 Mpc Gaussian smoothing for s. To highlight large scales, each final field is additionally smoothed externally with a R = 15h 1 Mpc Gauss catalog is obtained from a 1% random subsample of one RunPB N-body realization; the random catalog f randomly distributed particles. panel: Nonlinear density before reconstruction (divided by 3 for better visibility). Middle panels: Individ ift contributions to the Eulerian growth-shift reconstruction. Right panel: Expected excess density for grow
  • 100. ALL ALGORITHMS Performance comparison! EGS algorithm yields 95% of BAO S/N of standard Lagrangian LGS algorithm! EF2 algorithm similar, other algorithms significantly worse MS et al. 1508.06972! FIG. 9. Cumulative BAO signal-to-noise-squared for reconstructed power spectra as a function of kmax measured from sim- ulations for three Eulerian reconstruction algorithms (dashed colored) and the corresponding three Eulerian reconstruction algorithms (solid colored). See Section V for a summary of the algorithms. The signal-to-noise-squared of the density before reconstruction (black) and of the linear density (gray) are included for comparison. Growth-Shift F2 reconstruction Random-Random Reconstruction method LGS EGS LF2 EF2 LRR ERR Perfect NoRec BAO signal-to-noise 14.2 13.6 12.7 13.3 11.1 11.3 17.0 10.3 Compared against LGS ±0 % 4.7 % 11 % 6.3 % 22 % 21 % +19 % 27 % Compared against NoRec +38 % +31 % +23 % +29 % +6.9 % +8.8 % +64 % ±0 % TABLE II. Total BAO signal-to-noise for kmax = 0.4h/Mpc for various reconstruction algorithms (obtained from Fig. 9, based on simulations). ‘Perfect’ refers to the BAO signal-to-noise of the linear density and ‘NoRec’ to the BAO signal-to-noise of the measured nonlinear density without performing any reconstruction. The second-to-last row shows how much of the signal- to-noise is lost compared to performing the standard LGS reconstruction. The bottom row shows how much signal-to-noise is gained by reconstructions compared to performing no reconstruction. The EGS and EF2 methods may be useful for applications because they perform almost as well as the standard LGS algorithm and have potential advantages due to their Eulerian nature (e.g. they can be expressed as combinations of 2-, 3- and 4-point functions of the unreconstructed density). The LF2 method and the random-random methods are not competitive in terms of signal-to-noise so they should not be used. The performance of ERR can be improved by using asymmetric smoothing where only one of the fields entering quadratic fields is smoothed; in this case ERR performs almost as well as EF2 but still slightly worse. For the LRR method it is less obvious how to reduce the
  • 101. SMOOTHING SCALE Dependence on smoothing scale MS et al. 1508.06972! 2. Cumulative BAO signal-to-noise for varying Gaussian smoothing scale R, using scales up to kmax = 0.15h/M x = 0.4h/Mpc (right). Note that the shot noise in our setup is unrealistically small, ¯n 1 = 31Mpc3 /h3 .