An automatic detection method for small size dwarf galaxy candidates

¹ School of Computer Science and Technology, Taiyuan University of Science and Technology, Shanxi 030024, PR China
² Shanxi Key Laboratory of Big Data Analysis and Parallel Computing, Shanxi 030024, PR China
³ School of Computer Science and Technology, North University of China, Shanxi 030051, PR China
⁴ Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210008, PR China
⁵ School of Astronomy and Space Sciences, University of Science and Technology of China, Hefei 230026, PR China

^★ Corresponding author; jianghui@tyust.edu.cn

Received: 18 November 2024
Accepted: 27 February 2025

Abstract

The missing satellite problem remains a central issue of the Lambda cold dark matter (ΛCDM) model. On a small scale, the number of observed dwarf galaxies is still fewer than the number predicted by existing theories. Therefore, finding fainter dwarf galaxies in deeper images is crucial for refining the existing theoretical framework. In this study, we propose an end-to-end automatic identification scheme for small size and faint dwarf galaxies based on the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys photometric images, and we provide a batch of dwarf galaxy candidates. We develop a dwarf galaxy automatic detection model, YOLO-DG, based on the YOLOv7 framework, and we achieve a precision of 88.2% and a recall of 89.1% on the test set. We identify 742 251 dwarf galaxy candidates across the entire DESI DR9 footprint using YOLO-DG, with their spectral redshifts concentrated in the range of 0–0.1. The faintest dwarf galaxy candidates detected by YOLO-DG have magnitudes of 31.61, 27.62, and 32.78 mag in the g, r, and z bands, respectively. We identify 95 230 local volume dwarf galaxy candidates, 33 of which are identified based on spectral redshift. The half-light radius of the smallest local volume dwarf galaxy candidate is 0.31 arcsec. Finally, we provide a complete catalogue of local volume dwarf galaxy candidates.