Mixing Bayesian Techniques for Eﬀec6ve Real‐6me
Classiﬁca6on of Astronomical Transients

Ashish Mahabal, Caltech
Sapporo, ADASS, 6 Oct 2009

Overview

George Djorgovski
Ciro Donalek
Andrew Drake
Roy Williams
MaPhew Graham
Baback Moghaddam
Michael Turmon• Methods• Combining• Context• ServingTerry PratcheP in Equal Rites (1987)
Animals never spend 6me dividing experience
into liPle bits and specula6ng about all the bits
they've missed. The whole panoply of theuniverse has been neatly expressed to them asthings to(a) mate with,(b) eat,(c) run away from, and(d) rocks.

Ancient Astronomical context(a) mate with,(= specula6ve/sexy science)(b) eat,(= bread and buPer objects)(c) run away from,(= crazy cosmologies)
and(d) rocks(= transients)• Besides the known unknowns, wewant to look for unknown unknowns• We are forced to classify• And in Real‐Time!

Transforma6ons in astronomy

Shallow to deep; Small to large; Sporadic to repeated; more wavelengths

SN z=0.05
CSS 20090711

Data sizes and number of parameters• Moore’s law. Yes, but data sizes? 0.1 ‐> 10x TB• LSST: wide, fast and deep
• Hundreds of parameters for tens of millions ofobjects• Many studies possible (char6ng the solarsystem, Galaxy, universe at diﬀerent scales anddepths)• One of the exci6ng, emerging sub‐ﬁeld is of
temporal astronomy

The Palomar‐Quest Event Factory
tonight

Detect ~ 1 ‐ 2 *106 sources

baseline

per half‐night scanCompare withthe baseline sky

R

Find ~ 103 apparent
transients (in the data)

Remove instrum.
ar6facts
Iden6fy ~ 2 ‐ 4 *102 realtransients (on the sky)IRemoveasteroids
Iden6fy ~ 1 ‐ 10 possible
Astrophysical transients

Classiﬁca6on and follow‐up

Synop6c skysurveys: Opening up new dimensions
Being tackled in various ways:• BNs• GPRs(using colors, lightcurves, andcontextual informa6on like
Galac6c la6tude, proximity to agalaxy etc.)
Challenges (besides data mining):• Lots of follow‐up observing• Selec6ng candidates to follow(from tens of thousands per night)• In real‐6me!

Towards AutomatedEvent Classiﬁca6on
A necessity for large synop6csurveys

Eventparameters:
m1(t), m2(t),…
α, δ, µ, …
image shape…

contextual
informa6on

Event
Classiﬁca>on
EngineP(SN Ia) = …
P(SN II) = …
P(AGN) = …
P(CV) = …
P(dM) = …
….

Expert and ML generated priors

etc.
colorslightcurves

Classiﬁca6on
probabili6es(evolving,iterated)Transient classiﬁca6on mantra• Obtain a couple of epochs in one or more ﬁlters• Assign probabili6es for diﬀerent classes• Choose [to do more] observa6ons (ﬁlters,wavelengths) for best discrimina6on• Feed the new observa6ons back in• Revise probabili6es, choose observa6ons, …
• Based on conﬁrmed class revise priors

Status• Started with CRTS (P60 colors/lightcurves)• Applying to PTF (P60 colors/spectra)

hPp://www.astro.caltech.edu/P60FollowUp/

Radio + X‐ray

Building Bayesian Networks• Local dependencies,irrelevancies are evaluatedusing modeling• Priors, likelihoods areobtained• Directed Acyclic Graph isconstructed• Data deﬁne network• No “training” necessary

Radio, for instance8% CV classiﬁed as SN, 65% of objects classiﬁed as CV are actually CV
3 colors + gb
(WTA)

CV (0.65)

SN (0.71)

BL (0.33)

REST (0.23)

CV

0.72

0.08

0.08

0.13

SN

0.23

0.46

0.12

0.19

BL

0.24

0.03

0.49

0.24

REST

0.34

0.18

0.21

0.26

Priors based on CRTS data.
Delta‐m > 2

8% CV classiﬁed as SN, 65% of objects classiﬁed as CV are actually CV
3 colors + gb
(WTA)

CV (0.65)

SN (0.71)

BL (0.33)

REST (0.23)

CV

0.72

0.08

0.08

0.13

SN

0.23

0.46

0.12

0.19

BL

0.24

0.03

0.49

0.24

REST

0.34

0.18

0.21

0.26

TypeCVSNBLREST3,n,w

0.60

0.73

0.30

0.22

3,y,w

0.65

0.71

0.33

0.23

2+,n,w

0.58

0.77

0.31

0.17

2+y,w

0.65

0.78

0.41

0.19

3,y,50

0.75

0.83

0.37

0.19

3,y,40‐10

0.73

0.82

0.37

0.18

2+,y,50

0.74

0.88

0.43

0.19

• Adding gb helps (of course)• Having an addi6onal colors is good(generally)• More context info helps (distance tonearest galaxy, for instance)
3 colors + gb +galaxy prox.
(WTA)

CV (0.74)

SN (0.84)

BL (0.31)

CV0.74

0.08

0.16

SN

0.21

0.500.27BL

0.19

0.00

0.80

(1‐contam.)completenessUsing GPR with lightcurves
Given several epochs and corresponding
magnitudes, es6mate the likelihood of apar6cular magnitude for a new epoch(using some covariance func6on)

The 3 hyperparameters are “free” and are varied

The 3 hyperparameters are “free” and are variedM. Ebden

dmdt

Diﬃcult tool to train for periodic
variables.
132 Mira from ASAS:
Period: 55 – 523 days
Peak mag: 4.89 – 13.29
Amplitude: 1.63 – 6.65

More points => bePer performance

BNGPR

Next steps include combining
CRTS, P60, PTF etc. data

Fusion ModuleP

CV, SN,Blazars, Rest
spectralightcurves‐4 ‐> 4 (10 bins each)Poryolios, seman6c linking and skyalert(hPp://www.skyalert.org)• Ac6ve follow‐up– New images– New colors– BePer astrometry– Spectra• Passive follow‐up(annotators)– Galaxy distance– Classiﬁca6on• Program• ExpertFollow‐up (for missing values)• Such that it will help discriminate bePer• Serve probabili6es so that consumers can choose their typesof transients• Widest possible models• (resource uniformity)• (well connectedness)

Towards the Glass Bead Game

Natural and ar6ﬁcial classiﬁers; varied inputs; uniﬁed output
Diﬃcult but exci6ng problem
To boldly classify what no one has classiﬁed before …