Dust-lane spheroidal galaxies from DESI-LS

Recent studies propose that they form through minor mergers between ETGs and gas-rich dwarf galaxies, offering a window into the interstellar medium (ISM) of ETGs and star formation triggered by small-scale interactions. However, their rarity poses a challenge for assembling large, statistically robust samples via manual selection.

To overcome this limitation, we employ GC-SWGAN, a semisupervised learning method developed by Luo+ 2025ApJS..279...17L, to systematically identify DLSGs within the DESI Legacy Imaging Surveys (DESI-LS). The methodology involves training a generative adversarial network on unlabeled galaxy images to extract morphological features, followed by fine-tuning the model using a small data set of labeled DLSGs. In our experiments, despite DLSGs constituting only ~3.7% of the test set, GC-SWGAN achieves remarkable performance, with an 87% recall rate, 84% accuracy, and an F1 score of 86%, underscoring its efficacy for DLSG detection.