Update on morphology WP activities M. Huertas-Company (GAL-SWG - - - PowerPoint PPT Presentation

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Update on morphology WP activities M. Huertas-Company (GAL-SWG - - - PowerPoint PPT Presentation

Apr 09, 2023 215 likes •415 views

Update on morphology WP activities M. Huertas-Company (GAL-SWG - morphology) EUCLID France - 7 Janvier 2016 Morphology WP in a nutshell Legacy Galaxies WP Provide? Request? shape / morphology measurements for EUCLID galaxies France

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Update on morphology WP activities

  • M. Huertas-Company

(GAL-SWG - morphology)

EUCLID France - 7 Janvier 2016

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Morphology WP in a nutshell

  • Legacy Galaxies WP
  • Provide? Request? shape / morphology

measurements for EUCLID galaxies

  • France leadership - DUC (SWG) - DOLE (OU-

MER?)

  • Close relation with OU-MER (cataloguing), OU-SHE

(shape), OU-VIS (background), OU-SIR (size estimate required), OU-PHZ

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Morphology?

  • Star/galaxy separation - ALL OUs
  • Ellipticity, size, Sersic index, C, A, S, G - ALL OUs
  • B/T - Legacy: SWGs + OU-PHZ?
  • internal structure, clumps, spiral arms, merger

signatures, lenses? - Legacy: SWGs + OU-PHZ? +OU-SHE? Fundamental legacy value of EUCLID

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MORPH
 WP

OU-MER OU-SIM OU-VIS

Codes “Realistic” imaging

Which morphologies? Precision? Which codes? Test and provide algorithms

background “Euclidization” + Cataloguing

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Concentration (EUCLIDized CANDELS DC1 - OU-MER)

Unresolved/faint — very high concentration ETGs LTGs Irr CAS codes provided to OU- MER (MHC, Conselice) - test in progress

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Galaxy sizes: galfit

Unresolved objects Objects at z>1, faint/unresolved in EUCLID images…

MAIN ISSUE: EXECUTION TIME ~ 1 obj/sec Alternative algorithms: SExtractor model fitting ~20 obj/sec (singe Sersic) 40 times slower than detection mode Problem with neighbors — requires calibration OTHER POSSIBILITIES: ML, DL

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Big-data opportunities: DEEP LEARNING FOR EUCLID

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Deep convolutional neural networks

  • Hubel & Wiesel 1962 + LeCun 1998
  • Mimic the human brain
  • Learn non-linear features (from pixels!) using

hidden layers

  • Very expensive in computing time
  • GPUs…
  • Very popular, used by *all* the technology

giants (Google, Microsoft)

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Learning algorithm (Neural Network, SVM…)

DATA

Dimension reduction PCA or manual (colors, C, A, n …) morphs. photoz’s ….

N parameters

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Learning algorithm (Neural Network, SVM…)

DATA

Dimension reduction PCA or manual (colors, C, A, n …) morphs. photoz’s ….

N parameters

FEATURE LEARNING LAYERS

OPTIMAL FEATURES

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Gini-M20 plane (EUCLID emulated images)

Very noise/resolution dependent… ETGs LTGs Irr

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CONVNET for CANDELS Feature learning Neural Network INPUT: RGB JPEG GDS snapshots OUTPUT: 10 probs. 10

  • TRAIN: ~50.000 redundant galaxies

in GDS (~10 days)

  • CLASSIFY: GDN, COSMOS, UDS,

GDS (~8h/field)

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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 Unc VISUAL DOMINANT CLASS SPHEROID DISK IRR PS Unc AUTO DOMINANT CLASS

DOMINANT CLASS

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SPHEROIDS DISKS IRR PS UNC

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SPHEROIDS DISKS B+D D+I I/MERGER

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Classical ML + CAS

MHC+14a

20-30% contamination in a sample of ETGs at z>1 EUCLID

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Action items for 2016

  • First set of OU-SIM simulations should become available

(analytic profiles)

  • Enough for pursuing ellipticity, size etc algorithm testing
  • Pursue on deep-learning testing (simulations from HST +

numerical)

  • Detailed morphology classification
  • B/D, sizes etc ?
  • Good news: manpower available (Student+postdoc)