A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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
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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