Identification of White Dwarfs with Infrared Excesses in DESI DR1 Based on Deep Learning
White dwarfs (WDs) with infrared (IR) excesses probe dusty debris disks, low-mass companions, and the late-stage evolution of planetary and binary systems. Conventional searches usually rely on source-by-source spectral energy distribution (SED) fitting and visual inspection, which become time-consuming for the rapidly growing samples produced by large spectroscopic surveys. We develop a supervised multimodal deep learning framework for scalable preselection of IR-excess WD candidates in the Dark Energy Spectroscopic Instrument (DESI) Data Release 1. The model combines Pan-STARRS1 z- and y-band images, unWISE W1- and W2-band images, and atmospheric, astrometric, and photometric tabular features. On the internal validation set, the model achieved an area under the receiver operating characteristic curve of 0.9765, demonstrating effective separation of literature-reported IR-excess candidates from comparison WDs. Applied to the 10,988 objects in DESI-WD-SEARCH-DATA, the model selected 1886 first-stage candidates for subsequent validation. Image-based screening for Wide-field Infrared Survey Explorer (WISE)-scale contamination and composition-dependent SED validation identified 1041 objects satisfying the adopted excess criteria. Of these, 294 had sufficient photometric coverage for further assessment, and catalog-specific photometric-quality screening yielded a final catalog of 221 candidates, including 204 main-sample and 17 warning candidates. We also provide the complete list of 1886 first-stage candidates with flags recording the outcomes of subsequent screening steps. This catalog provides targets for future high-resolution IR imaging, spectroscopic follow-up, and studies of the physical origins of IR excesses around WDs.