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A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning

Steinhardt, Charles L. and Weaver, John R. and Maxfield, Jack and Davidzon, Iary and Faisst, Andreas L. and Masters, Dan and Schemel, Madeline and Toft, Sune (2020) A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning. Astrophysical Journal, 891 (2). Art. No. 136. ISSN 1538-4357. https://resolver.caltech.edu/CaltechAUTHORS:20200313-142323560

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Abstract

Large photometric surveys provide a rich source of observations of quiescent galaxies, including a surprisingly large population at z > 1. However, identifying large, but clean, samples of quiescent galaxies has proven difficult because of their near-degeneracy with interlopers such as dusty, star-forming galaxies. We describe a new technique for selecting quiescent galaxies based upon t-distributed stochastic neighbor embedding (t-SNE), an unsupervised machine-learning algorithm for dimensionality reduction. This t-SNE selection provides an improvement both over UVJ, removing interlopers that otherwise would pass color selection, and over photometric template fitting, more strongly toward high redshift. Due to the similarity between the colors of high- and low-redshift quiescent galaxies, under our assumptions, t-SNE outperforms template fitting in 63% of trials at redshifts where a large training sample already exists. It also may be able to select quiescent galaxies more efficiently at higher redshifts than the training sample.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3847/1538-4357/ab76beDOIArticle
https://arxiv.org/abs/2002.05729arXivDiscussion Paper
ORCID:
AuthorORCID
Steinhardt, Charles L.0000-0003-3780-6801
Weaver, John R.0000-0003-1614-196X
Davidzon, Iary0000-0002-2951-7519
Faisst, Andreas L.0000-0002-9382-9832
Masters, Dan0000-0001-5382-6138
Toft, Sune0000-0003-3631-7176
Additional Information:© 2020. The American Astronomical Society. Received 2019 June 13; revised 2020 February 12; accepted 2020 February 13; published 2020 March 13. The authors wish to thank Johann Bock Severin, Gabe Brammer, Beryl Hovis-Afflerbach, Adam Jermyn, Christian Kragh Jespersen, Vasily Kokorev, Allison Man, Georgios Magdis, and Jonas Vinther for useful discussions. C.L.S. and S.T. are supported by ERC grant 648179 "ConTExt." The Cosmic Dawn Center (DAWN) is funded by the Danish National Research Foundation under grant No. 140. J.M. is supported by the Jonathan Baker Excellence in Physics Fund.
Group:Infrared Processing and Analysis Center (IPAC)
Funders:
Funding AgencyGrant Number
European Research Council (ERC)648179
Danish National Research Foundation140
Jonathan Baker Excellence in Physics FundUNSPECIFIED
Subject Keywords:Astronomy data analysis ; Computational astronomy ; Galaxy quenching ; Quenched galaxies ; Galaxy classification systems ; Star formation
Issue or Number:2
Classification Code:Unified Astronomy Thesaurus concepts: Astronomy data analysis (1858); Computational astronomy (293); Galaxy quenching (2040); Quenched galaxies (2016); Galaxy classification systems (582); Star formation (1569)
Record Number:CaltechAUTHORS:20200313-142323560
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200313-142323560
Official Citation:Charles L. Steinhardt et al 2020 ApJ 891 136
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:101915
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:16 Mar 2020 14:08
Last Modified:16 Mar 2020 14:08

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