investigating galaxy evolution with deep learning
TRANSCRIPT
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Investigating galaxy evolution with deep
learningMarc Huertas-Company
Instituto de Astrofísica de CanariasObservatoire de Paris
Université Paris DiderotInstitut Universitaire de France
AAS 233rd meeting, Machine Learning in Astronomical Data Analysis, Seattle 2019
Berta Margalef-Bentabol, Fernando Caro, Christoph Lee, Alexandre Boucaud, Avishai Dekel, Joel Primack, Tom Charnock, Annalisa Pillepich, Vicente Rodriguez (+ TNG team), Emille Ishida (+ COIN initiative), Helena Dominguez-Sanchez …
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AI FEVER?GOOGLE SEARCH TRENDS
PUBLICATIONS
CONFERENCES
Source
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BEFORE 2012….
CAT?
DOG?
TRIVIAL HUMAN TASKS REMAINED CHALLENGING FOR COMPUTERS
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AFTER 2012
IT HAS BECOME TRIVIAL….
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MHC+15b
99.8
96.3
88.5
97.1
93.7
11.5 3.0 5.6
2.9 0.2
0.5 0.8
0.8
0.8
0.4
0.4
0.4
0.3
0.3
0.3
0.0
0.0
0.0
0.2
0.2
SPHEROID DISK IRR PS UncVISUAL DOMINANT CLASS
SPHEROID
DISK
IRR
PS
Unc
AUTO
DO
MIN
ANT
CLAS
S
9799
VISUAL
AUTOMATIC
“OUR CATS AND DOGS”: GALAXY MORPHOLOGY
CNNs DEEP LEARNING SOLVES
THE PROBLEMOF GALAXY MORPHOLOGICAL
CLASSIFICATION?
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TALK ON WEDNESDAY AFTERNOON -
SPECIAL SESSION ON “MODERN” GALAXY MORPHOLOGIES
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1. MOST OF THE PROCESSING WE DO ON IMAGES CAN BE DONE WITH AI - POSSIBLY MORE EFFICIENTLY AND MORE ACCURATELY
2. WE CAN LEARN SOME PHYSICS BY USING AI TO LINK SIMULATIONS AND OBSERVATIONS
2 TAKE HOME MESSAGES FOR TODAY
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IMAGE
DETECTED OBJECT
Boucaud+19
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GALAXY 1
GALAXY 1
IMAGE
DETECTED OBJECT
Boucaud+19
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GALAXY 1
GALAXY 1
IMAGE
DETECTED OBJECT
MEASUREMENTS
Photometry
Shape
…
Boucaud+19
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GALAXY 1
GALAXY 1
IMAGE
DETECTED OBJECT
MEASUREMENTS
Photometry
Shape
…
Boucaud+19
ISOLATED GALAXIES IN
CANDELS ARTIFICALLY
BLENDED
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U-NET(ENCODER DECODER)
IMAGE OF BLENDED OBJECTS
OUTPUT SEGMENTATION MAP (BINARY 2
CHANNELS)
VANILLA CNN
FLUX GALAXY 1
FLUX GALAXY 2
Boucaud+19
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U-NET+CNN SExtractor
MAG(CENTRAL)-MAG(COMPANION) MAG(CENTRAL)-MAG(COMPANION)
DE
LTA
MA
G (C
EN
TR
AL
)
DE
LTA
MA
G (C
EN
TR
AL
)
PHOTOMETRY OF BLENDED SOURCES
Boucaud, MHC+19
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MAG(CENTRAL)-MAG(COMPANION) MAG(CENTRAL)-MAG(COMPANION)
AVE
RA
GE
SC
ATT
ER
AVE
RA
GE
SC
ATT
ER
SExtractor
Deep Learning
CONSTANT <0.2 SCATTER. A FACTOR3-4 BETTER THAN SExtractor
REASONABLE BEHAVIOR FOR DIFFERENT MORPHOLOGIES
Boucaud, MHC+19
PHOTOMETRY OF BLENDED SOURCES
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Lee, MHC+19
DETECTION OF CLUMPS IN HIGH REDSHIFT GALAXIES
SIMILAR APPROACHES CAN BE USED FOR…
BULGE-DISK DECOMPOSITIONS
Tuccillo, MHC+19
BULGE+DISK BULGE
DISK
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THEORY / SIMULATIONS
[FULL 3D EVOLUTION HISTORY]
AI TO LINK THEORY AND OBSERVATION
IN THE DATA SPACEIllustris, EAGLE, Horizon-AGN …
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THEORY / SIMULATIONS
[FULL 3D EVOLUTION HISTORY]
PROJECT HYDRO SIMS IN
THE “OBSERVATIONAL
PLANE”
AI TO LINK THEORY AND OBSERVATION
IN THE DATA SPACEIllustris, EAGLE, Horizon-AGN …
ASSUMPTIONS OF MASS TO LIGHT CONVERSION
+ DUST +PSF
+ NOISE
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THEORY / SIMULATIONS
[FULL 3D EVOLUTION HISTORY]
MOCK OBSERVATIONS
PROJECT HYDRO SIMS IN
THE “OBSERVATIONAL
PLANE”
AI TO LINK THEORY AND OBSERVATION
IN THE DATA SPACEIllustris, EAGLE, Horizon-AGN …
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THEORY / SIMULATIONS
[FULL 3D EVOLUTION HISTORY]
OBSERVATIONSMOCK OBSERVATIONS
PROJECT HYDRO SIMS IN
THE “OBSERVATIONAL
PLANE”
MACHINE (DEEP)LEARNING
AI TO LINK THEORY AND OBSERVATION
IN THE DATA SPACEIllustris, EAGLE, Horizon-AGN …
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THEORY / SIMULATIONS
[FULL 3D EVOLUTION HISTORY]
OBSERVATIONSMOCK OBSERVATIONS
PROJECT HYDRO SIMS IN
THE “OBSERVATIONAL
PLANE”
MACHINE (DEEP)LEARNING
AI TO LINK THEORY AND OBSERVATION
IN THE DATA SPACE
TRAINTEST
Illustris, EAGLE, Horizon-AGN …
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THEORY / SIMULATIONS
[FULL 3D EVOLUTION HISTORY]
OBSERVATIONSMOCK OBSERVATIONS
PROJECT HYDRO SIMS IN
THE “OBSERVATIONAL
PLANE”
MACHINE (DEEP)LEARNING
AI TO LINK THEORY AND OBSERVATION
IN THE DATA SPACE
TRAIN TEST
Illustris, EAGLE, Horizon-AGN …
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SIMULATIONS OBSERVATIONS
SDSS DR7
CNN MODEL TO ESTIMATE GALAXY “VISUAL”
MORPHOLOGY
HOW REALISTIC ARE ILLUSTRIS
TNG MORPHOLOGIES?
Dominguez-Sanchez+18
TRAIN
Nair&Abraham10GZOO
ILLUTRIS TNG
MOCK SDSS
GALAXIES
CLASSIFY
MHC+19
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ILLUSTRIS ELLIPTICALS ILLUSTRIS S0s
ILLUSTRIS EARLY SPIRALS ILLUSTRIS LATE SPIRALS
MHC+19
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ILLUSTRIS ELLIPTICALS ILLUSTRIS S0s
ILLUSTRIS EARLY SPIRALS ILLUSTRIS LATE SPIRALS
MHC+19
ATTRIBUTION TECHNIQUES
PROVIDE SOME INFORMATION
BUT STILL RATHER LIMITED
ELL
SPIRAL
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THE HIGH MASS END OF THE STELLAR MASS FUNCTION OF
ILLUSTRIS TNG IS DOMINATED BY DISKS
MHC+19
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THE HIGH MASS END OF THE STELLAR MASS FUNCTION OF
ILLUSTRIS TNG IS DOMINATED BY DISKS
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MHC+18
Dekel&Burkert+14, Ceverino+15, Zolotov+15,Tacchella+16a,b, Dekel+18
COSMIC TIME
MAS
S
IDENTIFYING COMPACTION IN THE VELA ZOOM-IN OF SIMULATIONS
VELA suite of hydro simulations
[Ceverino, Dekel+]
SIMULATION OBSERVATIONS
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MHC+18
Dekel&Burkert+14, Ceverino+15, Zolotov+15,Tacchella+16a,b, Dekel+18
COSMIC TIME
MAS
S
LABELLING EXCLUSIVELY
BASED ON THE FULL HISTORY
OF THE GALAXY FROM ADVANCED
NUMERICAL SIMULATIONS
BASED ON PHYSICAL PRINCIPLES
PRE-BN BN POST-BN
IDENTIFYING COMPACTION IN THE VELA ZOOM-IN OF SIMULATIONS
MHC+18
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MHC+18
Dekel&Burkert+14, Ceverino+15, Zolotov+15,Tacchella+16a,b, Dekel+18
COSMIC TIME
MAS
SPRE-BN BN POST-BN
IN THE OBSERVATIONS WE ONLY HAVE ONE SNAPSHOT OF A GALAXY AT A GIVEN TIME
MHC+18
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MHC+18
Dekel&Burkert+14, Ceverino+15, Zolotov+15,Tacchella+16a,b, Dekel+18
COSMIC TIME
MAS
S
?
?
PRE-BN BN POST-BN
HOW CAN WE ESTIMATE THE PHASE FROM A UNIQUE IMAGE?
MHC+18
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[COSMOLOGICALSIMULATION]
USE THE FORMATION HISTORY OF EACH GALAXYTO LABEL IMAGES …
PRE- BLUE NUGGET
BLUE NUGGET
POST NUGGETPROJECT THE 3D OBJECT TO GENERATE “MOCK”
IMAGES AS OBSERVED BY HUBBLE
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VELA HIGH RESOLUTION
VELA HST RESOLUTION
CANDELS
MHC+18
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THE OUPUT SOFTMAX PROBABILITY TRACKS THE GAS MASS
SIMULATIONDEEP LEARNINGMHC+18
COSMIC TIME COSMIC TIME
MAS
S
SOFT
MAX
PR
OBA
BILI
TIES
POST-BN CLASSBN CLASS
PRE-BN CLASS
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CONSTRAINTS ON THE OBSERVABILITY TIMESCALE
POST-BN CLASSBN CLASSPRE-BN CLASS
MHC+18
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CAPTURING THE MODEL UNCERTAINTY
SEVERAL OPTIONS EXIST IN THE LITTERATURE…
NO TIME TO TALK ABOUT BNNs
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GENERATIVE ADVERSARIAL NETWORKS
TWO COMPETING NETWORKS
(Goodfellow+)
Every 2 iterations the generator is trained to force the discriminator
to classify as real
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GENERATIVE ADVERSARIAL NETWORKS
TWO COMPETING NETWORKS
(Goodfellow+)
Every 2 iterations the discriminator is trained to force to distinguish between
real and fake
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ANOMALY DETECTION WITH GANs
SIMULATIONS [EAGLE, Illustris, FIRE, VELA…]
OBSERVATIONS
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A PROOF-OF-CONCEPT CASE: UNSUPERVISED DETECTION OF
MERGERS WITH GANs
ANOMALY
NORMAL
MARGALEF-BENTABOL, MHC+2019
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MARGALEF-BENTABOL, MHC+2019
MERGERS OCCUPY A DIFFERENT REGION IN THE GAN GENERATED LATENT SPACE
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MARGALEF-BENTABOL, MHC+2019
(RECONSTRUCTED - ORIGINAL)
“ANOMALY SCORE”
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1. MOST OF THE PROCESSING WE DO ON IMAGES CAN BE DONE WITH AI - POSSIBLY MORE EFFICIENTLY AND MORE ACCURATELY
2. WE CAN LEARN SOME PHYSICS BY USING AI TO LINK SIMULATIONS AND OBSERVATIONS
2 TAKE HOME PROVOCATIVE ?
MESSAGES FOR TODAY
WITH SUPERVISED ML GOING BACK AND FORTH FROM SIMS TO DATA
WITH UN-SUPERVISED ML TO MEASURE SIMILARITIES
DETECTION, PHOTOMETRY, PHOTOZ’s, MORPHOMETRY …