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

  • Steinhardt C
  • Weaver J
  • Maxfield J
  • et al.
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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.

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APA

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

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