Searching for Quiescent Galaxies over $3 < z < 6$ in JWST Surveys Using Manifold Learning
Alexander de la Vega, Mitchell D. Babcock, Bahram Mobasher, Dominik A., Riemann, Nima Chartab, Shoubaneh Hemmati, Arianna S. Long, Sogol Sanjaripour

TL;DR
This paper introduces a machine learning approach using manifold learning to efficiently identify quiescent galaxies at redshifts 3 to 6 in JWST data, improving upon traditional color selection methods.
Contribution
The study applies the UMAP machine learning technique to pre-select high-redshift quiescent galaxies using JWST/NIRCam colors, reducing sample size and increasing efficiency over existing methods.
Findings
Identified 44 quiescent galaxy candidates from 43,926 in JADES.
Achieved five times fewer candidates than color-color selection.
Discovered many new quiescent galaxies, including very young ones.
Abstract
Quiescent galaxies over are rare and puzzling. They formed and quenched within two billion years and simulations routinely struggle to predict their observed abundances. Developing a robust identification technique for these galaxies is crucial for constraining galaxy evolution models. Traditional rest-frame color-color selection techniques for quiescent galaxies are known to break down or require adjustments at . Recently, observed-frame color-color criteria have been established with JWST/NIRCam colors that efficiently pre-select high-redshift quiescent galaxies using only of a given sample. In this work, the Uniform Manifold Approximation and Projection machine-learning technique is applied to pre-select quiescent galaxies over using observed NIRCam colors. From a parent sample of 43,926 galaxies in JADES, we ultimately find 44 quiescent…
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Taxonomy
TopicsAstronomy and Astrophysical Research · Astronomical Observations and Instrumentation · Galaxies: Formation, Evolution, Phenomena
