The JWST Resolved Stellar Populations Early Release Science Program III: Photometric Star-Galaxy Separations for NIRCam

TL;DR

This paper develops criteria using DOLPHOT photometric metrics and color information from JWST NIRCam images to accurately distinguish stars from unresolved background galaxies in deep stellar fields.

Contribution

It introduces a simple, effective method for star-galaxy separation in NIRCam data using existing DOLPHOT parameters and color criteria, eliminating the need for external tools.

Findings

DOLPHOT metrics can reliably classify stars in NIRCam data.

Color combinations between SW and LW filters effectively identify galaxies.

The method achieves high purity in star samples without complex heuristics.

Abstract

We present criteria for separately classifying stars and unresolved background galaxies in photometric catalogs generated with the point spread function (PSF) fitting photometry software DOLPHOT from images taken of Draco II, WLM, and M92 with the Near Infrared Camera (NIRCam) on JWST. Photometric quality metrics from DOLPHOT in one or two filters can recover a pure sample of stars. Conversely, colors formed between short-wavelength (SW) and long-wavelength (LW) filters can be used to effectively identify pure samples of galaxies. Our results highlight that the existing DOLPHOT output parameters can be used to reliably classify stars in our NIRCam data without the need to resort to external tools or more complex heuristics.