Deep learning at scale for the construction of galaxy catalogs in the Dark Energy Survey

Khan, Asad; Huerta, E. A.; Wang, Sibo; Gruendl, Robert; Jennings, Elise; Zheng, Huihuo

doi:10.1016/j.physletb.2019.06.009

Title: Deep learning at scale for the construction of galaxy catalogs in the Dark Energy Survey

Journal Article · 01 August 2019 · Physics Letters B

DOI: https://doi.org/10.1016/j.physletb.2019.06.009 · OSTI ID:1770004

; Huerta, E. A.; Wang, Sibo; Gruendl, Robert; Jennings, Elise;

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Sponsoring Organization:: USDOE

OSTI ID:: 1770004

Journal Information:: Physics Letters B, Journal Name: Physics Letters B Vol. 795 Journal Issue: C; ISSN 0370-2693

Publisher:: ElsevierCopyright Statement

Country of Publication:: Netherlands

Language:: English

References (14)

The Dark Energy Survey: more than dark energy – an overview Abbott, T.; Abdalla, F. B.; Aleksic, J. Monthly Notices of the Royal Astronomical Society, Vol. 460, Issue 2, p. 1270-1299 https://doi.org/10.1093/mnras/stw641	journal	March 2016
Morphological classification of galaxies using simple photometric parameters Doi, M.; Fukugita, M.; Okamura, S. Monthly Notices of the Royal Astronomical Society, Vol. 264, Issue 4 https://doi.org/10.1093/mnras/264.4.832	journal	October 1993
Galaxies, Human Eyes, and Artificial Neural Networks Lahav, O.; Naim, A.; Buta, R. J. Science, Vol. 267, Issue 5199 https://doi.org/10.1126/science.267.5199.859	journal	February 1995
Cosmological Results from High‐ z Supernovae Tonry, John L.; Schmidt, Brian P.; Barris, Brian The Astrophysical Journal, Vol. 594, Issue 1 https://doi.org/10.1086/376865	journal	September 2003
The Relationship between Stellar Light Distributions of Galaxies and Their Formation Histories Conselice, Christopher J. The Astrophysical Journal Supplement Series, Vol. 147, Issue 1 https://doi.org/10.1086/375001	journal	July 2003
Sdss-Iii: Massive Spectroscopic Surveys of the Distant Universe, the Milky way, and Extra-Solar Planetary Systems Eisenstein, Daniel J.; Weinberg, David H.; Agol, Eric The Astronomical Journal, Vol. 142, Issue 3 https://doi.org/10.1088/0004-6256/142/3/72	journal	August 2011
Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant Riess, Adam G.; Filippenko, Alexei V.; Challis, Peter The Astronomical Journal, Vol. 116, Issue 3 https://doi.org/10.1086/300499	journal	September 1998
New Constraints on Ω M , Ω Λ , and w from an Independent Set of 11 High‐Redshift Supernovae Observed with the Hubble Space Telescope Knop, R. A.; Aldering, G.; Amanullah, R. The Astrophysical Journal, Vol. 598, Issue 1 https://doi.org/10.1086/378560	journal	November 2003
Using MPI Gropp, William; Lusk, Ewing; Skjellum, Anthony MIT Press https://doi.org/10.7551/mitpress/7056.001.0001	book	January 1999
Neural computation as a tool for galaxy classification: methods and examples Lahav, O.; Nairn, A.; Sodre, L. Monthly Notices of the Royal Astronomical Society, Vol. 283, Issue 1 https://doi.org/10.1093/mnras/283.1.207	journal	October 1996
Measurements of Ω and Λ from 42 High‐Redshift Supernovae Perlmutter, S.; Aldering, G.; Goldhaber, G. The Astrophysical Journal, Vol. 517, Issue 2 https://doi.org/10.1086/307221	journal	June 1999
Galaxy Zoo 1: data release of morphological classifications for nearly 900 000 galaxies★: Galaxy Zoo Lintott, Chris; Schawinski, Kevin; Bamford, Steven Monthly Notices of the Royal Astronomical Society, Vol. 410, Issue 1 https://doi.org/10.1111/j.1365-2966.2010.17432.x	journal	November 2010
Galaxy Zoo: reproducing galaxy morphologies via machine learning★: Galaxy Zoo: morphology via machine learning Banerji, Manda; Lahav, Ofer; Lintott, Chris J. Monthly Notices of the Royal Astronomical Society, Vol. 406, Issue 1 https://doi.org/10.1111/j.1365-2966.2010.16713.x	journal	April 2010
Using transfer learning to detect galaxy mergers Ackermann, Sandro; Schawinski, Kevin; Zhang, Ce Monthly Notices of the Royal Astronomical Society, Vol. 479, Issue 1 https://doi.org/10.1093/mnras/sty1398	journal	May 2018

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