Discovery of Green Pea Galaxies in DESI Legacy Imaging Surveys using deep learning

ABSTRACT Green Pea galaxies (GPs) are compact, extreme emission-line galaxies that serve as valuable local analogues to high-redshift Ly $\alpha$ emitters and provide key insights into Lyman continuum escape. However, the census of known GPs remains small, limited by the difficulty of distinguishing them from point-like contaminants such as stars and quasars. While the wide-field DESI Legacy Imaging Surveys provide substantial data depth, the limited filter coverage ($g, r, z$) causes significant shifts in photometric colours, rendering traditional colour-selection criteria unreliable for identifying these targets. To address this issue, we propose MobileMamba-ECA, a novel lightweight deep learning framework that integrates State Space Models with Efficient Channel Attention (ECA) mechanisms to robustly identify GPs despite these spectral limitations. Comparative evaluations demonstrate that the model outperforms baseline methods, achieving an accuracy of 98.77 per cent and an F1-score of 98.94 per cent. By applying this method to the DESI Legacy Imaging Surveys and conducting spectroscopic confirmation, we identified a total of 775 GP candidates. Of these, we successfully recovered 48 previously recorded GPs and confirmed 216 newly identified GPs through spectroscopy. The remaining 511 candidates stand as promising targets for follow-up spectroscopic observation, significantly enhancing our sample of these rare star-forming galaxies and contributing to the broader understanding of their properties and distribution.