Papers › Searching for Quiescent Galaxies over 3 < z < 6 in JWST Surveys Using Manifold Learning
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
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Quiescent galaxies over 3<z<6 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 z≳3. Recently, observed-frame color-color criteria have been established with JWST/NIRCam colors that efficiently pre-select high-redshift quiescent galaxies using only ≲1% of a given sample. In this work, the Uniform Manifold Approximation and Projection machine-learning technique is applied to pre-select quiescent galaxies over 3<z<6 using observed NIRCam colors. From a parent sample of 43,926 galaxies in JADES, we ultimately find 44 quiescent candidates from a pool of ≈2,300 galaxies. This is about five times fewer galaxies than what would be pre-selected using color-color criteria. Two-thirds of these candidates can be pre-selected from a pool as small as 247, which is about twice as efficient as existing observed-frame color selection techniques. Nearly two-thirds of the candidates are new discoveries and include quiescent galaxies with mass-weighted ages as young as ≲300 Myr. We obtain number densities in agreement with the literature at z<4 and find generally higher abundances at z>4, although our measurements are consistent within errors. This technique may be applied to other JWST surveys.
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