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

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A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning. / Steinhardt, Charles L.; Weaver, John R.; Maxfield, Jack; Davidzon, Iary; Faisst, Andreas L.; Masters, Dan; Schemel, Madeline; Toft, Sune.

I: Astrophysical Journal, Bind 891, Nr. 2, 136, 01.03.2020.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Steinhardt, CL, Weaver, JR, Maxfield, J, Davidzon, I, Faisst, AL, Masters, D, Schemel, M & Toft, S 2020, 'A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning', Astrophysical Journal, bind 891, nr. 2, 136. https://doi.org/10.3847/1538-4357/ab76be

APA

Steinhardt, C. L., Weaver, J. R., Maxfield, J., Davidzon, I., Faisst, A. L., Masters, D., Schemel, M., & Toft, S. (2020). A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning. Astrophysical Journal, 891(2), [136]. https://doi.org/10.3847/1538-4357/ab76be

Vancouver

Steinhardt CL, Weaver JR, Maxfield J, Davidzon I, Faisst AL, Masters D o.a. A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning. Astrophysical Journal. 2020 mar. 1;891(2). 136. https://doi.org/10.3847/1538-4357/ab76be

Author

Steinhardt, Charles L. ; Weaver, John R. ; Maxfield, Jack ; Davidzon, Iary ; Faisst, Andreas L. ; Masters, Dan ; Schemel, Madeline ; Toft, Sune. / A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning. I: Astrophysical Journal. 2020 ; Bind 891, Nr. 2.

Bibtex

@article{bd11400f2a114a65b8a94a242995b3fa,
title = "A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning",
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.",
author = "Steinhardt, {Charles L.} and Weaver, {John R.} and Jack Maxfield and Iary Davidzon and Faisst, {Andreas L.} and Dan Masters and Madeline Schemel and Sune Toft",
year = "2020",
month = mar,
day = "1",
doi = "10.3847/1538-4357/ab76be",
language = "English",
volume = "891",
journal = "Astrophysical Journal",
issn = "0004-637X",
publisher = "Institute of Physics Publishing, Inc",
number = "2",

}

RIS

TY - JOUR

T1 - A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning

AU - Steinhardt, Charles L.

AU - Weaver, John R.

AU - Maxfield, Jack

AU - Davidzon, Iary

AU - Faisst, Andreas L.

AU - Masters, Dan

AU - Schemel, Madeline

AU - Toft, Sune

PY - 2020/3/1

Y1 - 2020/3/1

N2 - 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.

AB - 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.

U2 - 10.3847/1538-4357/ab76be

DO - 10.3847/1538-4357/ab76be

M3 - Journal article

VL - 891

JO - Astrophysical Journal

JF - Astrophysical Journal

SN - 0004-637X

IS - 2

M1 - 136

ER -

ID: 240307639