The document provides information about a workshop on classification of astronomical time-series data held at UC Berkeley on May 7, 2012. It discusses the challenges of classifying large amounts of time-series data from current and upcoming astronomical surveys, and how machine learning can help address these challenges by making rapid classifications of light curves to identify interesting transient events and variable object types. Specifically, it describes how machine learning has been used to identify supernovae in real-time from large surveys, and how features extracted from light curves can be used to classify variable stars and other astronomical objects.
2. Classification of Astronomical
Time-Series Data in the
Synoptic Survey Era
Josh Bloom
Joseph Richards
University of California, Berkeley
Berkeley Streaming Workshop; 7 May 2012
3. Center for Time-Domain Informatics
UC Berkeley (UCB):
Faculty/Staff
JSB, Dan Starr (Astro), John Rice, Noureddine El Karoui (Stats), Martin
Wainwright, Masoud Nikravesh (CS)
Postdocs
Joey Richards (stat/astro), Berian James, Damian Eads, Dovi Poznanski
(→Tel Aviv), Brad Cenko, Nat Butler, Nino Cucchiara, Damian Eads
(→Cambridge)
Grad Students
Dan Perley (→Caltech), Adam Miller, Adam Morgan, Chris Klein, James
Long, Tamara Broderick (stats), Sahand Negahban (EECS), John Brewer (→Yale),
Henrik Brink (←Copenhagen)
Undergrads
Anthony Paredes, Tatyana Gavrilchenko, Stuart Gegenheimer, Maxime Rischard,
Justin Higgins, Rachel Kennedy, Arien Crellin-Quick, Michelle Kislak (→UCLA),
Allison Merritt (→Yale)
Lawrence Berkeley National Laboratory (LBNL):
Peter Nugent, David Schlegel, Nic Ross, Horst Simon
Visit our website: http://cftd.info/
5. Text
Understanding & Exploiting the Dynamic Universe
•Twinkle, twinkle...
Everything changes at some level (brightness, color, position, ...)
6. Text
Understanding & Exploiting the Dynamic Universe
•Twinkle, twinkle...
Everything changes at some level (brightness, color, position, ...)
• Stars die...and blow up
supernovae, gamma-ray bursts, new phenomena ...
7. Text
Understanding & Exploiting the Dynamic Universe
•Twinkle, twinkle...
Everything changes at some level (brightness, color, position, ...)
• Stars die...and blow up
supernovae, gamma-ray bursts, new phenomena ...
• Discovery is only the start
8. Text
Understanding & Exploiting the Dynamic Universe
•Twinkle, twinkle...
Everything changes at some level (brightness, color, position, ...)
• Stars die...and blow up
supernovae, gamma-ray bursts, new phenomena ...
• Discovery is only the start
Greatest insights require follow-up (imaging,
spectroscopy, archive introspection)
9. Text
Understanding & Exploiting the Dynamic Universe
•Twinkle, twinkle...
Everything changes at some level (brightness, color, position, ...)
• Stars die...and blow up
supernovae, gamma-ray bursts, new phenomena ...
• Discovery is only the start
Greatest insights require follow-up (imaging,
spectroscopy, archive introspection)
Follow-up is EXPENSIVE
(ie., people, time, telescope, resources, $)
13. Gamma-Ray Burst Transients
• Short-lived blasts of
high energy light
(γ-rays & X-rays)
• random & rare - found
by specialized satellites
“static” γ-ray
“static” γ-ray
sky
14. Gamma-Ray Burst Transients
• Short-lived blasts of 106
high energy light
(γ-rays & X-rays)
10,000
• random & rare - found
by specialized satellites
100
• also: brightest optical
events in universe
power
1
(transient “afterglow”)
0.01
15. Gamma-Ray Burst Transients
• Short-lived blasts of 106
high energy light
(γ-rays & X-rays)
10,000
• random & rare - found
by specialized satellites
100
• also: brightest optical
events in universe
power
1
(transient “afterglow”)
• two origins: exploding 0.01
massive stars &
colliding compact
objects
16. Gamma-Ray Burst Transients
• Short-lived blasts of 106
high energy light
(γ-rays & X-rays)
10,000
• random & rare - Challenge: how can we
found
by specialized satellites
maximize our science
100
return power
• also: brightest optical on discovery with
events in universe optimized follow up?
1
(transient “afterglow”)
• two origins: exploding 0.01
massive stars &
colliding compact
objects
17. Follow-Up-Resource-Aware Classification
collect burst
data from
satellite feed
predict which
events are
“high redshift”
"high redshift"
GRBs
in real-time
less interesting
unclassified "immediately"
available data
18. Follow-Up-Resource-Aware Classification
Efficiency vs α
1.0
predicted
fraction of high-redshift GRBs
improvement
(90% c.l.)
0.8
Fraction of high (z>4) GRBs observed
“59% (86%) of
high-z GRBs can
0.6
be captured from
om
following up the
nd
top 20% (40%) of 0.4
ra
the ranked
candidates”
0.2
0.0
0.0 0.2 0.4 0.6 0.8 1.0
followed-up fraction
Fraction of GRBs Followed Up: α
Morgan+11 reduced
19. Extragalactic Transient Universe:
Explosive Systems
-22
-20
Pair Production Supernovae
z=0.45
log(brightness)
-18 Type Ia
MH
-16
Type IIp
-14
IMBH + WD Collision 200Mpc
-12
NS + RSG Collision
NS + NS Mergers
-10
50 100 150 200
Days Since Explosion
E. Ramirez-Ruiz (UCSC)
20. Text
Data Deluge Challenge
Large Synoptic Survey Telescope (LSST) - 2018
! Light curves for 800M sources every 3 days
106 supernovae/yr, 105 eclipsing binaries
3.2 gigapixel camera, 20 TB/night
LOFAR & SKA
150 Gps (27 Tflops) → 20 Pps (~100 Pflops)
Gaia space astrometry mission - 2013
1 billion stars observed ∼70 times over 5 years
Will observe 20K supernovae
Many other astronomical surveys are already producing data:
SDSS, PTF, CRTS, Pan-STARRS, Hipparcos, OGLE, ASAS,
Kepler, LINEAR, DES (soon) etc., etc.
21. Text
Data Deluge Challenge
Large Synoptic Survey Telescope (LSST) - 2018
! Light curves for 800M sources every 3 days
106 supernovae/yr, 105 eclipsing binaries
3.2 gigapixel camera, 20 TB/night
LOFAR & SKA
150 Gps (27 Tflops) → 20 Pps (~100 Pflops)
Gaia space astrometry mission - 2013
1 billion stars observed ∼70 times over 5 years
Will observe 20K supernovae
Many other astronomical surveys are already producing data:
SDSS, PTF, CRTS, Pan-STARRS, Hipparcos, OGLE, ASAS,
Kepler, LINEAR, DES (soon) etc., etc.
22. Text
Data Deluge Challenge
Large Synoptic Survey Telescope (LSST) - 2018
! Light curves for 800M sources every 3 days
106 supernovae/yr, 105 eclipsing binaries
3.2 gigapixel camera, 20 TB/night
LOFAR & SKAHow do we do discovery,
follow-up, and inference when
150 Gps (27 Tflops) → 20 Pps (~100 Pflops) the
data rates (& requisite
Gaia space astrometry mission - 2013
1 billiontimescales) precludeyears
stars observed ∼70 times over 5 human
involvement?
Will observe 20K supernovae
Many other astronomical surveys are already producing data:
SDSS, PTF, CRTS, Pan-STARRS, Hipparcos, OGLE, ASAS,
Kepler, LINEAR, DES (soon) etc., etc.
23. Machine Learning As Surrogate
- trained to quickly make concrete, deterministic, &
repeatable statements about abstract concepts
“Is this varying
source astrophysical
in nature or
spurious?”
24. Machine Learning As Surrogate
- trained to quickly make concrete, deterministic, &
repeatable statements about abstract concepts
“Is this varying
source astrophysical Discovery
in nature or
spurious?”
25. Machine Learning As Surrogate
- trained to quickly make concrete, deterministic, &
repeatable statements about abstract concepts
PTF: 1.5M candidate/night
“Is this varying 1:1000 are astrophysical
source astrophysical
machine has opined on
in nature or 800M candidates
Bloom+11
spurious?” Poznanski, Brink, this workshop
Discovery
26. Reference New Difference
11kly
11kx
also, cf., Bailey+07
27. 2011fe identified w/ Machine-Learned Discovery Algorithms
Discovery image was
~11 hours after
explosion
Within a few hours, a
spectrum confirmed it
to be a SN Ia
Nearest SN Ia in more
than 3 decades
5th brightest supernova
in 100 years
28. Machine Learning As Surrogate
- trained to quickly make concrete, deterministic, &
repeatable statements about abstract concepts
“What is the nature
(origin/reason...) of
the variability?”
29. Machine Learning As Surrogate
- trained to quickly make concrete, deterministic, &
repeatable statements about abstract concepts
“What is the nature
Classification
(origin/reason...) of
the variability?”
30. Pulsating
Alpha Cygni (ACYG) Short Period (BCEPS)
Beta Cephei (BCEP) Anomalous (BLBOO)
Pulsating Stars
Multiple Modes (CEPB)
Cepheids (CEP)
Long Period (CWA)
W Virginis (CW) Short Period (CWB)
Delta Cep (DCEP) Symmetrical (DCEPS)
Delta Scuti (DSCT) Low Amplitude (DSCTC)
Slow Irregular (L) Late Spectral Type (K, M, C, S) (LB)
Mira (M) Supergiants (LC)
Dual Mode (RRB)
PV Telescopii (PVTEL)
Asymmetric (RRAB)
RR Lyrae (RR) Near Symmetric (RRC)
Constant Mean Magnitude (RVA)
RV Tauri (RV)
Variable Mean Magnitude (RVB)
Persistent Periodicity (SRA)
Semiregular (SR) Poorly Defined Periodicity (SRB)
Pulsating Subdwarfs (SXPHE) Supergiants (SRC)
F, G, or K (SRD)
Only H Absorption (ZZA)
ZZ Ceti (ZZ)
Only He Absorption (ZZB)
HeII Absoption (ZZO)
Cataclysmic Variables
Cataclysmic Variables
SS Cygni
U Geminorum (UG) SU Ursae Majoris SNIa
Z Camelopardalis
SNIb
Type I Supernovae (SNI)
SNIc
Supernovae (SN)
SNIIL
Type II Supernovae (SNII)
SNIIN
Fast Novae (NA)
Slow Novae (NB) SNIIP
Novae (N) Very Slow Novae (NC)
Novalike Variables (NL)
Recurrent Novae (NR)
Gamma-ray Bursts (GRB) Long Gamma-ray Burst (LSB)
Soft Gamma-ray Repeater (SGR)
Symbiotic Variables (ZAND) Short Gamma-ray Burst (SHB)
Eclipsing
Eclipsing Systems
Systems with White Dwarfs (WD)
Semidetached (SD) Early (O-A) (KE)
RS Canum Venaticorum (RS)
W Ursa Majoris (KW)
Planetary Nebulae (PN)
Contact Systems (K) Algol (Beta Persei) (EA)
Systems with Supergiant(s) (GS)
Eclipsing Binary Systems (E) Beta Lyrae (EB)
W Ursae Majoris (EW)
Main Sequence (DM)
Detached (D) With Subgiant (DS)
Detached - AR Lacertae (AR)
W Ursa Majoris (DW)
Wolf-Rayet Stars (WR)
33. Considerable Complications with Time-Series Data
• noisy, irregularly
sampled
• spurious data
• telltale signature
event may not
have happened
yet
34. Machine-Learning Approach to Classification
Features: homogenize the data; real-number metrics that
describe the time-domain characteristics & context of a source
~100 features computed in < 1 sec (including periodogram
analysis)
Wózniak et al. 2004; Protopapas+06, Willemsen & Eyer 2007; Debosscher et al. 2007; Mahabal et al.
2008; Sarro et al. 2009; Blomme et al. 2010; Kim+11, Richards+11
35. Machine-Learning Approach to Classification
Features: homogenize the data; real-number metrics that
describe the time-domain characteristics & context of a source
~100 features computed in < 1 sec (including periodogram
analysis)
periodic
variability metrics: e.g. domi metrics
nant freq :
e.g. Stetson indices, χ 2/dof Lomb- uencies in
Scargle, p
hase offs
(constant hypothesis) between ets
periods
shape analysis
wness, kurtosis, context metrics
e.g. ske
Gaussianity e.g. distance to nearest galaxy,
type of nearest galaxy, locatio
n
in the ecliptic plane
Wózniak et al. 2004; Protopapas+06, Willemsen & Eyer 2007; Debosscher et al. 2007; Mahabal et al.
2008; Sarro et al. 2009; Blomme et al. 2010; Kim+11, Richards+11
39. StructuredLearning
Structured Classification
Structured Classification: Let class taxonomy guide classifier.
5% gross mis-
classification
rate!
HSC: Hierarchical single-label HMC: Hierarchical multi-label
classification. classification.
I Fit separate classifier at I Fit one classifier, where
depth
each non-terminal node. L(y , f (x)) w0
Richards+11
40. Decision Boundaries are Survey Specific
How do we transfer learning from one survey to the next?
–3–
(a) (b)
feature #2
feature #1 feature #1
Hipparcos OGLE-III
Fig. 1.— (a) The grey lines represent the CART classifier constructed using Hipparcos data.
The points are Hipparcos sources. This classifier separates Hipparcos sources well (0.6%
error as measured by cross-validation). (b) Here the OGLE sources are plotted over the
Long+12; Richards+11
same decision boundaries. There is now significant class overlap (30% error rate). This is
41. Decision Boundaries are Survey Specific
– 31 –
How do we transfer learning from one survey to the next?
“Expert”
ASAS
(testing)
OGLE+Hip
(training)
Fig. 8.—
Long+12; Richards+11
Active learning samples on a single iteration of the algorithm. Yellow circles
signify points that at least 65% of users were able to classify. These points are included
42. Decision Boundaries are Survey Specific
How do we transfer learning from one survey to the next?
● ●
●
●
●
0.40
●
●
●
●
Percent of Confident ASAS RF Labels
●
●
0.35
●
●
●
0.30
●
0.25
●
●
0.20
●
0.15
●
2 4 6 8 0 2 4 6 8
AL Iteration AL Iteration
Long+12;
eft: Percent agreement of the Random Forest classifier with the ACVS labels, Richards+11
of AL iteration. Right: Percent of ASAS data with confident RF classification
43. Classification Statements are Inherently Fuzzy
- classification probabilities should reflect
uncertainty in the data & training
- higher confidence with greater proximity to training data
- calibration of classification probability vector
E.g.: 20% of transients classified as
supernova of type “Ib” with P=0.2
should be supernova of type “Ib”
44. Classification Statements are Inherently Fuzzy
- classification probabilities should reflect
uncertainty in the data & training
- higher confidence with greater proximity to training data
- calibration of classification probability vector
E.g.: 20% of transients classified as
supernova of type “Ib” with P=0.2
should be supernova of type “Ib”
Catalogs of Transients and
Variable Stars Must Become
Probabilistic
46. Doing Science with Probabilistic Catalogs
Demographics (with little followup):
trading high purity at the cost of lower efficiency
e.g., using RRL to find new Galactic structure
Novelty Discovery (with lots of followup):
trading high efficiency for lower purity
e.g., discovering new instances of rare classes
47. Discovery of Bright Galactic R Coronae Borealis and DY Persei
Variables: Rare Gems Mined from ASAS
A. A. Miller1,⇤ , J. W. Richards1,2 , J. S. Bloom1 , S. B. Cenko1 , J. M. Silverman1 ,
arXiv:1204.4181v1 [astro-ph.SR] 18 Apr 2012 D. L. Starr1 , and K. G. Stassun3,4
ABSTRACT
– 13 –
We present the results of a machine-learning (ML) based search for new R
Coronae Borealis (RCB) stars and DY Persei-like stars (DYPers) in the Galaxy
using cataloged light curves obtained by the All-Sky Automated Survey (ASAS).
RCB stars—a rare class of hydrogen-deficient carbon-rich supergiants—are of
great interest owing to the insights they can provide on the late stages of stellar
evolution. DYPers are possibly the low-temperature, low-luminosity analogs to
the RCB phenomenon, though additional examples are needed to fully estab-
lish this connection. While RCB stars and DYPers are traditionally identified
by epochs of extreme dimming that occur without regularity, the ML search
framework more fully captures the richness and diversity of their photometric
behavior. We demonstrate that our ML method recovers ASAS candidates that
would have been missed by traditional search methods employing hard cuts on
amplitude and periodicity. Our search yields 13 candidates that we consider
likely RCB stars/DYPers: new and archival spectroscopic observations confirm
that four of these candidates are RCB stars and four are DYPers. Our discovery
of four new DYPers increases the number of known Galactic DYPers from two
to six; noteworthy is that one of the new DYPers has a measured parallax and is
m ⇡ 7 mag, making it the brightest known DYPer to date. Future observations
of these new DYPers should prove instrumental in establishing the RCB con-
Fig. 2.— ASAS V nection. We consider these results, derived from a machine-learned probabilistic
-band light curves of newly discovery RCB stars and
DYPers. Note t
di↵ering magnitude ranges shown for each light curve. Spectroscopic observations confi
1
Department of Astronomy, University of California, Berkeley, CA 94720-3411, USA
the top four candidates to RCB/DY California, Berkeley, CA, bottomUSA are DYPers.
17 known Galactic be Universitystars, while the 94720-7450, four
Statistics Department,
RCB of Per 2
3
48. Discovery of Bright Galactic R Coronae Borealis and DY Persei
Variables: Rare Gems Mined from ASAS
A. A. Miller1,⇤ , J. W. Richards1,2 , J. S. Bloom1 , S. B. Cenko1 , J. M. Silverman1 ,
arXiv:1204.4181v1 [astro-ph.SR] 18 Apr 2012 D. L. Starr1 , and K. G. Stassun3,4
ABSTRACT
We present the results of a machine-learning (ML) based search for new R
Coronae Borealis (RCB) stars and DY Persei-like stars (DYPers) in the Galaxy
using cataloged light curves obtained by the All-Sky Automated Survey (ASAS).
RCB stars—a rare class of hydrogen-deficient carbon-rich supergiants—are of
great interest owing to the insights they can provide on the late stages of stellar
evolution. DYPers are possibly the low-temperature, low-luminosity analogs to
the RCB phenomenon, though additional examples are needed to fully estab-
lish this connection. While RCB stars and DYPers are traditionally identified
by epochs of extreme dimming that occur without regularity, the ML search
framework more fully captures the richness and diversity of their photometric
behavior. We demonstrate that our ML method recovers ASAS candidates that
would have been missed by traditional search methods employing hard cuts on
amplitude and periodicity. Our search yields 13 candidates that we consider
likely RCB stars/DYPers: new and archival spectroscopic observations confirm
that four of these candidates are RCB stars and four are DYPers. Our discovery
of four new DYPers increases the number of known Galactic DYPers from two
to six; noteworthy is that one of the new DYPers has a measured parallax and is
m ⇡ 7 mag, making it the brightest known DYPer to date. Future observations
of these new DYPers should prove instrumental in establishing the RCB con-
nection. We consider these results, derived from a machine-learned probabilistic
1
Department of Astronomy, University of California, Berkeley, CA 94720-3411, USA
17 known Galactic RCB/DY California, Berkeley, CA, 94720-7450, USA
Statistics Department, University of Per
2
3
49. Variety of Open Questions
1. How do bootstrap learning from one survey to
the next, given inherent differences?
“active learning” (e.g., Richards+11b)
2. How do we detect and quantify real outliers?
e.g. clustering, semi-supervised learning
(e.g., Protopapas+06, Rebbapragada+09, Bhattacharyya+, in prep)
3. How do imbue domain knowledge into
classifiers? hybridization, metalearning
4. How do we weigh classification value with
computational cost? resource allocation
50. Summary
science maximization with synoptic surveys demands a
more distant human role than before
machine learning in time-domain astrophysics is
not just talk...it’s working and enabling novel
science
yet, real-time discovery & classification is far
from solved
helpful to view endeavor as a resource-limited problem
51. See you tomorrow!
food starting 8am
talks starting 9am
group picture before lunch
Hinweis der Redaktion
* time-domain in astronomy\n* Crucial. new discoveries Looking at the sky with new tools (new eyes). Ptlometic order - planets were suppose to be fixed spherical orbs not with their own moons -- that didn&#x2019;t fit the world view. opportunistic tools\n* emphasizes the crucial roles of humans both in the data collection side, data analysis, and inference. \n\n\n
\n\n
happy to acknowledge. big effort. industry support.\n
needle in the haystack\n
needle in the haystack\n
needle in the haystack\n
needle in the haystack\n
needle in the haystack\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
contrained in time. decisions with incomplete information. Extreme rarities -- maybe a few a year of interest. imbalance and robust\n
\n
Teaming with things we know and dont know about. exploration of the known and the unknowns.\n\nRumsfeldian\nshort timescales.\n
\n
Simply must understand that our roles must change.\n
Simply must understand that our roles must change.\n
identification different than discovery: Galeilio \nGalileo's drawings show that he first observed Neptune on December 28, 1612, and again on January 27, 1613. On both occasions, Galileo mistook Neptune for a fixed star when it appeared very close&#x2014;inconjunction&#x2014;to Jupiter in the night sky;[20] hence, he is not credited with Neptune's discovery.\n\n253 year later\n\nJohann Gottfried Galle\n 23 September 1846\n
identification different than discovery: Galeilio \nGalileo's drawings show that he first observed Neptune on December 28, 1612, and again on January 27, 1613. On both occasions, Galileo mistook Neptune for a fixed star when it appeared very close&#x2014;inconjunction&#x2014;to Jupiter in the night sky;[20] hence, he is not credited with Neptune's discovery.\n\n253 year later\n\nJohann Gottfried Galle\n 23 September 1846\n
identification different than discovery: Galeilio \nGalileo's drawings show that he first observed Neptune on December 28, 1612, and again on January 27, 1613. On both occasions, Galileo mistook Neptune for a fixed star when it appeared very close&#x2014;inconjunction&#x2014;to Jupiter in the night sky;[20] hence, he is not credited with Neptune's discovery.\n\n253 year later\n\nJohann Gottfried Galle\n 23 September 1846\n
identification different than discovery: Galeilio \nGalileo's drawings show that he first observed Neptune on December 28, 1612, and again on January 27, 1613. On both occasions, Galileo mistook Neptune for a fixed star when it appeared very close&#x2014;inconjunction&#x2014;to Jupiter in the night sky;[20] hence, he is not credited with Neptune's discovery.\n\n253 year later\n\nJohann Gottfried Galle\n 23 September 1846\n
1.5 M per night, \n
\n
it should be easy -- there&#x2019;s a bunch of classes of objects which vary, we measure their light curves and that&#x2019;s it. Even remarkably homogeneous classes such as Ia and RRL exhibit huge variations. \n\n
it should be easy -- there&#x2019;s a bunch of classes of objects which vary, we measure their light curves and that&#x2019;s it. Even remarkably homogeneous classes such as Ia and RRL exhibit huge variations. \n\n
it should be easy -- there&#x2019;s a bunch of classes of objects which vary, we measure their light curves and that&#x2019;s it/\n
however, in practice\n
however, in practice\n
however, in practice\n
however, in practice\n
however, in practice\n
however, in practice\n
dynamic time warping\nhundreds of features: n log n, n^2, etc. some of these are results of external queries.\n\nSame things we, as experts, look at in a light curve and ancillary data.\n
dynamic time warping\nhundreds of features: n log n, n^2, etc. some of these are results of external queries.\n\nSame things we, as experts, look at in a light curve and ancillary data.\n
dynamic time warping\nhundreds of features: n log n, n^2, etc. some of these are results of external queries.\n\nSame things we, as experts, look at in a light curve and ancillary data.\n
dynamic time warping\nhundreds of features: n log n, n^2, etc. some of these are results of external queries.\n\nSame things we, as experts, look at in a light curve and ancillary data.\n
discovery of physical intuition, like what Alex talked about.\n
discovery of physical intuition, like what Alex talked about.\n
\n
how you observed the data impacts what you think it is. This is obvious.\n\napproach is to craft ground truth from one survey to look like another. Either in light curve space\nor in feature space.\n
how you observed the data impacts what you think it is. This is obvious.\n\napproach is to craft ground truth from one survey to look like another. Either in light curve space\nor in feature space.\n
how you observed the data impacts what you think it is. This is obvious.\n\napproach is to craft ground truth from one survey to look like another. Either in light curve space\nor in feature space.\n
how you observed the data impacts what you think it is. This is obvious.\n\napproach is to craft ground truth from one survey to look like another. Either in light curve space\nor in feature space.\n
say good bye to black and white catalogs, \n
posterior probabilities\nnot liklihoods -- convolved with the priors\nprescription for adapation\n
best way to find needles in teh haystack is to get really good a finding and identifying hay.\n
8 of 13\n&#x2206;mV up to &#x223C;8 mag), aperiodic declines in brightness\nAt maximum light RCB stars are bright supergiants,\n\nMerrill-Sanford bands of SiC2 in three of our candidates: ASAS 162232&#x2212;5349.2, ASAS 065113+0222.1, and ASAS 182658+0109.0. To our knowledge this is the first identification of SiC2 in a DYPer spectrum\n\n
last one not so important if we can wait for the answer.\n