A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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.
Originalsprog | Engelsk |
---|---|
Artikelnummer | 136 |
Tidsskrift | Astrophysical Journal |
Vol/bind | 891 |
Udgave nummer | 2 |
Antal sider | 12 |
ISSN | 0004-637X |
DOI | |
Status | Udgivet - 1 mar. 2020 |
Links
- http://arxiv.org/pdf/2002.05729
Indsendt manuskript
- http://adsabs.harvard.edu/abs/2020ApJ...891..136S
ID: 240307639