Cosmic Evolution Early Release Science Survey (CEERS): Multi-classing Galactic Dwarf Stars in the deep JWST/NIRCam
B.W. Holwerda, Chih-Chun Hsu, Nimish Hathi, Laura Bisigello, Alexander, de la Vega, Pablo Arrabal Haro, Micaela Bagley, Mark Dickinson, Steven L., Finkelstein, Jeyhan S. Kartaltepe, Anton M. Koekemoer, Casey Papovich, Nor, Pirzkal, Kyle Cook, Clayton Robertson, Caitlin M Casey

TL;DR
This study uses JWST/NIRCam data to identify and classify a large sample of galactic dwarf stars in the CEERS survey, demonstrating photometric classification methods and analyzing their spatial distribution.
Contribution
It presents a novel catalog of (sub)stellar dwarfs identified in deep JWST/NIRCam images using a kNN classifier trained on brown dwarf spectra, extending the depth and accuracy of previous surveys.
Findings
Identified 518 stellar objects down to m_F200W ~28.
Achieved ~95% classification precision and recall for broad spectral types.
Found scale heights consistent with previous literature for different dwarf types.
Abstract
Low mass (sub)stellar objects represent the low end of the initial mass function, the transition to free-floating planets and a prominent interloper population in the search for high-redshift galaxies. Without proper motions or spectroscopy, can one identify these objects photometrically? JWST/NIRCam has several advantages over HST/WFC3 NIR: more filters, a greater wavelength range, and greater spatial resolution. Here, we present a catalogue of (sub)stellar dwarfs identified in the Cosmic Evolution Early Release Science Survey (CEERS). We identify 518 stellar objects down to using half-light radius, a full three magnitudes deeper than typical HST/WFC3 images. A kNN nearest neighbour algorithm identifies and types these sources, using four HST/WFC3 and four NIRCam filters, trained on SpeX spectra of nearby brown dwarfs. The kNN with four neighbors classifies well…
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Taxonomy
TopicsAstronomy and Astrophysical Research · Stellar, planetary, and galactic studies · Gamma-ray bursts and supernovae
