21cm cosmology with ML

Hayato Shimabukuro
Hayato ShimabukuroAssociate professor um Yunnan university
21cm cosmology with machine learning
Hayato Shimabukuro(島袋隼⼠)
(Yunnan university, Nagoya university)
@21cm cosmology workshop (2023/7/16-7/22)
21cm line emission
Tb =
TS T
1 + z
(1 exp(⌧⌫))
⇠ 27xH(1 + m)
✓
H
dvr/dr + H
◆ ✓
1
T
TS
◆ ✓
1 + z
10
0.15
⌦mh2
◆1/2 ✓
⌦bh2
0.023
◆
[mK]
Brightness temperature (21cm signal)
Red : cosmology Blue : astrophysics
21cm line emission
We can map the distribution of HI in the IGM with 21cm line.
Liu & Shaw (2020)
To describe 21cm signal statistically…
Redshift
21cm line emission
We can map the distribution of HI in the IGM with 21cm line.
Liu & Shaw (2020)
21cm global signal: Sky-averaged 21cm line
signal
To describe 21cm signal statistically…
Redshift
We can map the distribution of HI in the IGM with 21cm line.
Redshift
To describe 21cm signal statistically…
Liu & Shaw (2020)
21cm line emission
We can map the distribution of HI in the IGM with 21cm line.
21cm line power spectrum
h Tb(k) Tb(k
0
)i = (2⇡)3
(k + k
0
)P21
Redshift
To describe 21cm signal statistically…
Liu & Shaw (2020)
21cm line emission
Statistical challenge in 21cm cosmology
Cosmology
Statistical challenge in 21cm cosmology
Cosmology 21cm cosmology
Statistical challenge in 21cm cosmology
Cosmology 21cm cosmology
We can learn astrophysics if EoR
parameters are constrained.
Yinzhe Ma’s talk
HERA collaboration 2022b
Astrophysics from 21cm upper limits
•We obtained some constraints on astrophysical
parameters from 21cm power spectrum upper limits.
•HERA firstly constrains X-ray parameters at high
redshift.
•High redshift galaxies suggest higher X-ray luminosity
and lower metallicity than local galaxies.
Astrophysics with 21cm line signal is
around the corner!?
Shimabukuro+ (2023)
Yinzhe Ma’s talk
HERA collaboration 2022b
Astrophysics from 21cm upper limits
•We obtained some constraints on astrophysical
parameters from 21cm power spectrum upper limits.
•HERA firstly constrains X-ray parameters at high
redshift.
•High redshift galaxies suggest higher X-ray luminosity
and lower metallicity than local galaxies.
Astrophysics with 21cm line signal is
around the corner!?
Applications of machine
learning(ML)
Application of ML to 21cm studies
Hey, ChatGPT, Please tell me how we can apply ML to 21cm studies.
1. Image reconstruction
2.Signal detection and
characterization
3. Data analysis and
feature extraction
4.Simulation and
modeling
5. Foreground subtraction
Application of ML to 21cm studies
Hey, ChatGPT, Please tell me how we can apply ML to 21cm studies.
Seems correct?
1. Image reconstruction
2.Signal detection and
characterization
3. Data analysis and
feature extraction
4.Simulation and
modeling
5. Foreground subtraction
•Training network with training
dataset, ANN can approximate any
function which associates input and
output values.
y = f(x)
• Applying trained network to unknown
data(test data) for prediction.
yANN = f(xtest)
• ANN consists of input layer, hidden
layer and output layer. Each layer has
neurons.
nonlinear regression Problem
Artificial Neural Network (ANN)
https://www.snexplores.org/article/explainer-what-is-a-neuron
•Training network with training
dataset, ANN can approximate any
function which associates input and
output values.
y = f(x)
• Applying trained network to unknown
data(test data) for prediction.
yANN = f(xtest)
• ANN consists of input layer, hidden
layer and output layer. Each layer has
neurons.
nonlinear regression Problem
Artificial Neural Network (ANN)
•Emulator + MCMC→parameter estimate
•Direct parameter estimate
•Distinguish EoR sources
(e.g) Hassan+ (2019)
•Others
(e.g.) Mertens+ (2017), Li+ (2019), Chardin+ (2019), Yoshiura+ (2020), Shimabukuro+
(2022), Bianco+ (2021,2023)
(e.g.) Kern+ (2017), Schmit+ (2018), Aviad+ (2020), Bevins+ (2021), Yoshiura+ (2023)
(e.g.) Shimabukuro&Semelin (2017), Gilet+ (2018), Nicolas+ (2019), Doussot+
(2019), Choudhury+(2020,2021,2022), Zhao+ (2022a,b), Vivekanand+ (2023)
21cm study + ML
•Emulator + MCMC→parameter estimate
•Direct parameter estimate
•Distinguish EoR sources
(e.g) Hassan+ (2019)
•Others
(e.g.) Mertens+ (2017), Li+ (2019), Chardin+ (2019), Yoshiura+ (2020), Shimabukuro+
(2022), Bianco+ (2021,2023)
(e.g.) Kern+ (2017), Schmit+ (2018), Aviad+ (2020), Bevins+ (2021), Yoshiura+ (2023)
(e.g.) Shimabukuro&Semelin (2017), Gilet+ (2018), Nicolas+ (2019), Doussot+
(2019), Choudhury+(2020,2021,2022), Zhao+ (2022a,b), Vivekanand+ (2023)
21cm study + ML
Parameter estimate (inverse problem)
(input)
21cmPS
(output)
EoR parameters
10
20
30
40
50
60
10 20 30 40 50 60
R
mfp,ANN
[Mpc]
Rmfp,true[Mpc]
10
20
30
40
50
60
10 20 30 40 50 60
ζ
ANN
ζtrue
1
10
100
1 10 100
T
vir,ANN
[K/10
3
]
Tvir,true[K/103
]
Shimabukuro &
Semelin (2017)
Input : 21cm PS + thermal noise(SKA)
See also Yi Mao’s talk
Parameter estimate (inverse problem)
(input)
21cmPS
(output)
EoR parameters
10
20
30
40
50
60
10 20 30 40 50 60
R
mfp,ANN
[Mpc]
Rmfp,true[Mpc]
10
20
30
40
50
60
10 20 30 40 50 60
ζ
ANN
ζtrue
1
10
100
1 10 100
T
vir,ANN
[K/10
3
]
Tvir,true[K/103
]
Shimabukuro &
Semelin (2017)
Input : 21cm PS + thermal noise(SKA)
(Choudhury+2022)
Input : 21cm PS +
Foreground + thermal
noise(SKA)
See also Yi Mao’s talk
parameters
21cm light-cone CNN
(input) (output)
Image > Parameters
(Gillet +2018)
•Emulator + MCMC→parameter estimate
•Parameter estimate
•Distinguish EoR sources
(e.g) Hassan+ (2019)
•Others
(e.g.) Mertens+ (2017), Li+ (2019), Chardin+ (2019), Yoshiura+ (2020), Shimabukuro+
(2022), Bianco+ (2021,2023)
(e.g.) Kern+ (2017), Schmit+ (2018), Aviad+ (2020), Bevins+ (2021), Yoshiura+ (2023)
(e.g.) Shimabukuro&Semelin (2017), Gilet+ (2018), Nicolas+ (2019), Doussot+
(2019), Choudhury+(2020,2021,2022), Zhao+ (2022a,b), Vivekanand+ (2023)
21cm study + ML
•Emulator + MCMC→parameter estimate
•Parameter estimate
•Distinguish EoR sources
(e.g) Hassan+ (2019)
•Others
(e.g.) Mertens+ (2017), Li+ (2019), Chardin+ (2019), Yoshiura+ (2020), Shimabukuro+
(2022), Bianco+ (2021,2023)
(e.g.) Kern+ (2017), Schmit+ (2018), Aviad+ (2020), Bevins+ (2021), Yoshiura+ (2023)
(e.g.) Shimabukuro&Semelin (2017), Gilet+ (2018), Nicolas+ (2019), Doussot+
(2019), Choudhury+(2020,2021,2022), Zhao+ (2022a,b), Vivekanand+ (2023)
21cm study + ML
Recovering statistics from another statistics
•We can associate statistics with other statistics by the ANN. Can we recover statistics from
another statistics by the ANN?
Statistics A Statistics B
Recovering statistics from another statistics
•We can associate statistics with other statistics by the ANN. Can we recover statistics from
another statistics by the ANN?
Statistics A Statistics B
21cm power spectrum
Recovering statistics from another statistics
•We can associate statistics with other statistics by the ANN. Can we recover statistics from
another statistics by the ANN?
Statistics A Statistics B
21cm power spectrum Bubble size distribution
(BSD)
Recovering statistics from another statistics
21cm power spectrum
Bubble size distribution
(BSD)
(Note) It does not mean 21cm PS contains beyond Gaussian information. Training enables us
to connect a data space to another data space.
IFT
21cm Image
BSD
Incomplete IFT due to limited number of antenna in interferometer.
visibility
•However, we do not observe 21cm image directly by radio interferometer!
•We first observe visibility and perform Inverse Fourier Transformation (IFT) to obtain
21cm image. Then, compute BSD.
Bubble size distribution(BSD)
Giri+ (2017)
•Bubble size distribution(BSD) tells us the information on EoR sources, ionizing efficiency,
radiative feedback, etc. We can measure BSD from 21cm image map.
IFT
21cm Image
BSD
Incomplete IFT due to limited number of antenna in interferometer.
visibility
•However, we do not observe 21cm image directly by radio interferometer!
•We first observe visibility and perform Inverse Fourier Transformation (IFT) to obtain
21cm image. Then, compute BSD.
Bubble size distribution(BSD)
Giri+ (2017)
•Bubble size distribution(BSD) tells us the information on EoR sources, ionizing efficiency,
radiative feedback, etc. We can measure BSD from 21cm image map.
21cm power spectrum BSD
visibility
We can directly compute 21cm power spectrum from visibility without Inverse Fourier
Transformation.
Avoid information loss by incomplete IFT.
Bubble size distribution(BSD)
Giri+ (2017)
21cm power spectrum BSD
visibility
We can directly compute 21cm power spectrum from visibility without Inverse Fourier
Transformation.
Can we recover BSD from 21cm PS ?
Avoid information loss by incomplete IFT.
Bubble size distribution(BSD)
Giri+ (2017)
Black: Distribution obtained by
21cm 3D image directly.
Red: Distribution obtained by
ANN.
Recovering BSD from 21cm PS
Recovering BSD from 21cm PS
HS, Mao, Tan (2022)
Including thermal noise
21cm PS with thermal noises
(SKA level)
Errors are estimated by 10
realizations thermal noises
Reconstruction of HI distribution from LAE
map is marked in angles (degrees) and the projected distances (comoving megaparsecs).
Fig. 5. Same as Figure 4, but for the LAEs z = 6.6. The large red open squares indicate the LAEs with spatially extended Lyα emission including Himiko
(Ouchi et al. 2009a) and CR7 (Sobral et al. 2015). See Shibuya et al. (2017b) for more details.
Input :
Lyman-alpha emitter galaxies
Output :
HI distribution
Yoshiura, HS +2021
cGAN
Our review paper
Shimabukuro+(2023)
PASJ
21cm cosmology with ML
21cm cosmology + machine learning
has big potential!
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21cm cosmology with ML

  • 1. 21cm cosmology with machine learning Hayato Shimabukuro(島袋隼⼠) (Yunnan university, Nagoya university) @21cm cosmology workshop (2023/7/16-7/22)
  • 2. 21cm line emission Tb = TS T 1 + z (1 exp(⌧⌫)) ⇠ 27xH(1 + m) ✓ H dvr/dr + H ◆ ✓ 1 T TS ◆ ✓ 1 + z 10 0.15 ⌦mh2 ◆1/2 ✓ ⌦bh2 0.023 ◆ [mK] Brightness temperature (21cm signal) Red : cosmology Blue : astrophysics
  • 3. 21cm line emission We can map the distribution of HI in the IGM with 21cm line. Liu & Shaw (2020) To describe 21cm signal statistically… Redshift
  • 4. 21cm line emission We can map the distribution of HI in the IGM with 21cm line. Liu & Shaw (2020) 21cm global signal: Sky-averaged 21cm line signal To describe 21cm signal statistically… Redshift
  • 5. We can map the distribution of HI in the IGM with 21cm line. Redshift To describe 21cm signal statistically… Liu & Shaw (2020) 21cm line emission
  • 6. We can map the distribution of HI in the IGM with 21cm line. 21cm line power spectrum h Tb(k) Tb(k 0 )i = (2⇡)3 (k + k 0 )P21 Redshift To describe 21cm signal statistically… Liu & Shaw (2020) 21cm line emission
  • 7. Statistical challenge in 21cm cosmology Cosmology
  • 8. Statistical challenge in 21cm cosmology Cosmology 21cm cosmology
  • 9. Statistical challenge in 21cm cosmology Cosmology 21cm cosmology We can learn astrophysics if EoR parameters are constrained.
  • 10. Yinzhe Ma’s talk HERA collaboration 2022b Astrophysics from 21cm upper limits •We obtained some constraints on astrophysical parameters from 21cm power spectrum upper limits. •HERA firstly constrains X-ray parameters at high redshift. •High redshift galaxies suggest higher X-ray luminosity and lower metallicity than local galaxies. Astrophysics with 21cm line signal is around the corner!? Shimabukuro+ (2023)
  • 11. Yinzhe Ma’s talk HERA collaboration 2022b Astrophysics from 21cm upper limits •We obtained some constraints on astrophysical parameters from 21cm power spectrum upper limits. •HERA firstly constrains X-ray parameters at high redshift. •High redshift galaxies suggest higher X-ray luminosity and lower metallicity than local galaxies. Astrophysics with 21cm line signal is around the corner!?
  • 13. Application of ML to 21cm studies Hey, ChatGPT, Please tell me how we can apply ML to 21cm studies. 1. Image reconstruction 2.Signal detection and characterization 3. Data analysis and feature extraction 4.Simulation and modeling 5. Foreground subtraction
  • 14. Application of ML to 21cm studies Hey, ChatGPT, Please tell me how we can apply ML to 21cm studies. Seems correct? 1. Image reconstruction 2.Signal detection and characterization 3. Data analysis and feature extraction 4.Simulation and modeling 5. Foreground subtraction
  • 15. •Training network with training dataset, ANN can approximate any function which associates input and output values. y = f(x) • Applying trained network to unknown data(test data) for prediction. yANN = f(xtest) • ANN consists of input layer, hidden layer and output layer. Each layer has neurons. nonlinear regression Problem Artificial Neural Network (ANN) https://www.snexplores.org/article/explainer-what-is-a-neuron
  • 16. •Training network with training dataset, ANN can approximate any function which associates input and output values. y = f(x) • Applying trained network to unknown data(test data) for prediction. yANN = f(xtest) • ANN consists of input layer, hidden layer and output layer. Each layer has neurons. nonlinear regression Problem Artificial Neural Network (ANN)
  • 17. •Emulator + MCMC→parameter estimate •Direct parameter estimate •Distinguish EoR sources (e.g) Hassan+ (2019) •Others (e.g.) Mertens+ (2017), Li+ (2019), Chardin+ (2019), Yoshiura+ (2020), Shimabukuro+ (2022), Bianco+ (2021,2023) (e.g.) Kern+ (2017), Schmit+ (2018), Aviad+ (2020), Bevins+ (2021), Yoshiura+ (2023) (e.g.) Shimabukuro&Semelin (2017), Gilet+ (2018), Nicolas+ (2019), Doussot+ (2019), Choudhury+(2020,2021,2022), Zhao+ (2022a,b), Vivekanand+ (2023) 21cm study + ML
  • 18. •Emulator + MCMC→parameter estimate •Direct parameter estimate •Distinguish EoR sources (e.g) Hassan+ (2019) •Others (e.g.) Mertens+ (2017), Li+ (2019), Chardin+ (2019), Yoshiura+ (2020), Shimabukuro+ (2022), Bianco+ (2021,2023) (e.g.) Kern+ (2017), Schmit+ (2018), Aviad+ (2020), Bevins+ (2021), Yoshiura+ (2023) (e.g.) Shimabukuro&Semelin (2017), Gilet+ (2018), Nicolas+ (2019), Doussot+ (2019), Choudhury+(2020,2021,2022), Zhao+ (2022a,b), Vivekanand+ (2023) 21cm study + ML
  • 19. Parameter estimate (inverse problem) (input) 21cmPS (output) EoR parameters 10 20 30 40 50 60 10 20 30 40 50 60 R mfp,ANN [Mpc] Rmfp,true[Mpc] 10 20 30 40 50 60 10 20 30 40 50 60 ζ ANN ζtrue 1 10 100 1 10 100 T vir,ANN [K/10 3 ] Tvir,true[K/103 ] Shimabukuro & Semelin (2017) Input : 21cm PS + thermal noise(SKA) See also Yi Mao’s talk
  • 20. Parameter estimate (inverse problem) (input) 21cmPS (output) EoR parameters 10 20 30 40 50 60 10 20 30 40 50 60 R mfp,ANN [Mpc] Rmfp,true[Mpc] 10 20 30 40 50 60 10 20 30 40 50 60 ζ ANN ζtrue 1 10 100 1 10 100 T vir,ANN [K/10 3 ] Tvir,true[K/103 ] Shimabukuro & Semelin (2017) Input : 21cm PS + thermal noise(SKA) (Choudhury+2022) Input : 21cm PS + Foreground + thermal noise(SKA) See also Yi Mao’s talk
  • 21. parameters 21cm light-cone CNN (input) (output) Image > Parameters (Gillet +2018)
  • 22. •Emulator + MCMC→parameter estimate •Parameter estimate •Distinguish EoR sources (e.g) Hassan+ (2019) •Others (e.g.) Mertens+ (2017), Li+ (2019), Chardin+ (2019), Yoshiura+ (2020), Shimabukuro+ (2022), Bianco+ (2021,2023) (e.g.) Kern+ (2017), Schmit+ (2018), Aviad+ (2020), Bevins+ (2021), Yoshiura+ (2023) (e.g.) Shimabukuro&Semelin (2017), Gilet+ (2018), Nicolas+ (2019), Doussot+ (2019), Choudhury+(2020,2021,2022), Zhao+ (2022a,b), Vivekanand+ (2023) 21cm study + ML
  • 23. •Emulator + MCMC→parameter estimate •Parameter estimate •Distinguish EoR sources (e.g) Hassan+ (2019) •Others (e.g.) Mertens+ (2017), Li+ (2019), Chardin+ (2019), Yoshiura+ (2020), Shimabukuro+ (2022), Bianco+ (2021,2023) (e.g.) Kern+ (2017), Schmit+ (2018), Aviad+ (2020), Bevins+ (2021), Yoshiura+ (2023) (e.g.) Shimabukuro&Semelin (2017), Gilet+ (2018), Nicolas+ (2019), Doussot+ (2019), Choudhury+(2020,2021,2022), Zhao+ (2022a,b), Vivekanand+ (2023) 21cm study + ML
  • 24. Recovering statistics from another statistics •We can associate statistics with other statistics by the ANN. Can we recover statistics from another statistics by the ANN? Statistics A Statistics B
  • 25. Recovering statistics from another statistics •We can associate statistics with other statistics by the ANN. Can we recover statistics from another statistics by the ANN? Statistics A Statistics B 21cm power spectrum
  • 26. Recovering statistics from another statistics •We can associate statistics with other statistics by the ANN. Can we recover statistics from another statistics by the ANN? Statistics A Statistics B 21cm power spectrum Bubble size distribution (BSD)
  • 27. Recovering statistics from another statistics 21cm power spectrum Bubble size distribution (BSD) (Note) It does not mean 21cm PS contains beyond Gaussian information. Training enables us to connect a data space to another data space.
  • 28. IFT 21cm Image BSD Incomplete IFT due to limited number of antenna in interferometer. visibility •However, we do not observe 21cm image directly by radio interferometer! •We first observe visibility and perform Inverse Fourier Transformation (IFT) to obtain 21cm image. Then, compute BSD. Bubble size distribution(BSD) Giri+ (2017) •Bubble size distribution(BSD) tells us the information on EoR sources, ionizing efficiency, radiative feedback, etc. We can measure BSD from 21cm image map.
  • 29. IFT 21cm Image BSD Incomplete IFT due to limited number of antenna in interferometer. visibility •However, we do not observe 21cm image directly by radio interferometer! •We first observe visibility and perform Inverse Fourier Transformation (IFT) to obtain 21cm image. Then, compute BSD. Bubble size distribution(BSD) Giri+ (2017) •Bubble size distribution(BSD) tells us the information on EoR sources, ionizing efficiency, radiative feedback, etc. We can measure BSD from 21cm image map.
  • 30. 21cm power spectrum BSD visibility We can directly compute 21cm power spectrum from visibility without Inverse Fourier Transformation. Avoid information loss by incomplete IFT. Bubble size distribution(BSD) Giri+ (2017)
  • 31. 21cm power spectrum BSD visibility We can directly compute 21cm power spectrum from visibility without Inverse Fourier Transformation. Can we recover BSD from 21cm PS ? Avoid information loss by incomplete IFT. Bubble size distribution(BSD) Giri+ (2017)
  • 32. Black: Distribution obtained by 21cm 3D image directly. Red: Distribution obtained by ANN. Recovering BSD from 21cm PS
  • 33. Recovering BSD from 21cm PS HS, Mao, Tan (2022)
  • 34. Including thermal noise 21cm PS with thermal noises (SKA level) Errors are estimated by 10 realizations thermal noises
  • 35. Reconstruction of HI distribution from LAE map is marked in angles (degrees) and the projected distances (comoving megaparsecs). Fig. 5. Same as Figure 4, but for the LAEs z = 6.6. The large red open squares indicate the LAEs with spatially extended Lyα emission including Himiko (Ouchi et al. 2009a) and CR7 (Sobral et al. 2015). See Shibuya et al. (2017b) for more details. Input : Lyman-alpha emitter galaxies Output : HI distribution Yoshiura, HS +2021 cGAN
  • 38. 21cm cosmology + machine learning has big potential!