White dwarf-main sequence (WDMS) binary systems are essential probes for understanding binary stellar evolution and play a pivotal role in constraining theoretical models of various transient phenomena. In this study, we construct a catalog of WDMS binaries using Gaia DR3's low-resolution BP/RP (XP) spectra. Our approach integrates a model-independent neural network for spectral modelling with Gaussian Process Classification to accurately identify WDMS binaries among over 10 million stars within 1 kpc. This study identify approximately 30,000 WDMS binary candidates, including ~1,700 high-confidence systems confirmed through spectral fitting. Our technique is shown to be effective at detecting systems where the main-sequence star dominates the spectrum - cases that have historically challenged conventional methods. Validation using GALEX photometry reinforces the reliability of our classifications: 70\% of candidates with an absolute magnitude MG>7 exhibit UV excess, a characteristic signature of white dwarf companions. Our all-sky catalog of WDMS binaries expands the available dataset for studying binary evolution and white dwarf physics and sheds light on the formation of WDMS.
@article{arxiv.2501.14494,
title = {Identification of 30,000 White Dwarf-Main Sequence binaries candidates from Gaia DR3 BP/RP(XP) low-resolution spectra},
author = {Jiadong Li and Yuan-Sen Ting and Hans-Walter Rix and Gregory M. Green and David W. Hogg and Juan-Juan Ren and Johanna Müller-Horn and Rhys Seeburger},
journal= {arXiv preprint arXiv:2501.14494},
year = {2025}
}