Abstract

Dust-lane spheroidal galaxies (DLSGs) are unique astrophysical systems that exhibit the morphology of early-type galaxies (ETGs) but are distinguished by prominent dust lanes. 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 semi-supervised learning method developed by Z. Luo et al. (2025a), to systematically identify DLSGs within the DESI Legacy Imaging Surveys (DESI-LS). The methodology involves training a generative adversarial network (GAN) on unlabeled galaxy images to extract morphological features, followed by fine-tuning the model using a small dataset 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. Applying this model to ∼310,000 DESI-LS galaxies that meet the criteria mr < 17.0 and 0.01 < z < 0.07 we compile the largest catalog of DLSG candidates to date, identifying 9,482 dust-lane ETGs. A preliminary analysis reveals that these DLSGs exhibit significantly redder g −r colors and higher specific star formation rates compared to non-DLSGs. This catalog enables future studies of ISM properties in ETGs and the role of minor mergers in driving star formation in the nearby universe.

Abstract

Dust-lane spheroidal galaxies (DLSGs) are unique astrophysical systems that exhibit the morphology of early-type galaxies (ETGs) but are distinguished by prominent dust lanes. 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 semi-supervised learning method developed by Z. Luo et al. (2025a), to systematically identify DLSGs within the DESI Legacy Imaging Surveys (DESI-LS). The methodology involves training a generative adversarial network (GAN) on unlabeled galaxy images to extract morphological features, followed by fine-tuning the model using a small dataset 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. Applying this model to ∼310,000 DESI-LS galaxies that meet the criteria mr < 17.0 and 0.01 < z < 0.07 we compile the largest catalog of DLSG candidates to date, identifying 9,482 dust-lane ETGs. A preliminary analysis reveals that these DLSGs exhibit significantly redder g −r colors and higher specific star formation rates compared to non-DLSGs. This catalog enables future studies of ISM properties in ETGs and the role of minor mergers in driving star formation in the nearby universe.

Abstract

Dust-lane spheroidal galaxies (DLSGs) are unique astrophysical systems that exhibit the morphology of early-type galaxies (ETGs) but are distinguished by prominent dust lanes. 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 semi-supervised learning method developed by Z. Luo et al. (2025a), to systematically identify DLSGs within the DESI Legacy Imaging Surveys (DESI-LS). The methodology involves training a generative adversarial network (GAN) on unlabeled galaxy images to extract morphological features, followed by fine-tuning the model using a small dataset 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. Applying this model to ∼310,000 DESI-LS galaxies that meet the criteria mr < 17.0 and 0.01 < z < 0.07 we compile the largest catalog of DLSG candidates to date, identifying 9,482 dust-lane ETGs. A preliminary analysis reveals that these DLSGs exhibit significantly redder g −r colors and higher specific star formation rates compared to non-DLSGs. This catalog enables future studies of ISM properties in ETGs and the role of minor mergers in driving star formation in the nearby universe.

Abstract

Dust-lane spheroidal galaxies (DLSGs) are unique astrophysical systems that exhibit the morphology of early-type galaxies (ETGs) but are distinguished by prominent dust lanes. 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 semi-supervised learning method developed by Z. Luo et al. (2025a), to systematically identify DLSGs within the DESI Legacy Imaging Surveys (DESI-LS). The methodology involves training a generative adversarial network (GAN) on unlabeled galaxy images to extract morphological features, followed by fine-tuning the model using a small dataset 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. Applying this model to ∼310,000 DESI-LS galaxies that meet the criteria mr < 17.0 and 0.01 < z < 0.07 we compile the largest catalog of DLSG candidates to date, identifying 9,482 dust-lane ETGs. A preliminary analysis reveals that these DLSGs exhibit significantly redder g −r colors and higher specific star formation rates compared to non-DLSGs. This catalog enables future studies of ISM properties in ETGs and the role of minor mergers in driving star formation in the nearby universe.