From Galaxy Zoo DECaLS to BASS/MzLS: detailed galaxy morphology classification with unsupervised domain adaption
Journal:
arXiv
Published Date:
Dec 20, 2024
Abstract
The DESI Legacy Imaging Surveys (DESI-LIS) comprise three distinct surveys:
the Dark Energy Camera Legacy Survey (DECaLS), the Beijing-Arizona Sky Survey
(BASS), and the Mayall z-band Legacy Survey (MzLS). The citizen science project
Galaxy Zoo DECaLS 5 (GZD-5) has provided extensive and detailed morphology
labels for a sample of 253,287 galaxies within the DECaLS survey. This dataset
has been foundational for numerous deep learning-based galaxy morphology
classification studies. However, due to differences in signal-to-noise ratios
and resolutions between the DECaLS images and those from BASS and MzLS
(collectively referred to as BMz), a neural network trained on DECaLS images
cannot be directly applied to BMz images due to distributional mismatch. In
this study, we explore an unsupervised domain adaptation (UDA) method that
fine-tunes a source domain model trained on DECaLS images with GZD-5 labels to
BMz images, aiming to reduce bias in galaxy morphology classification within
the BMz survey. Our source domain model, used as a starting point for UDA,
achieves performance on the DECaLS galaxies' validation set comparable to the
results of related works. For BMz galaxies, the fine-tuned target domain model
significantly improves performance compared to the direct application of the
source domain model, reaching a level comparable to that of the source domain.
We also release a catalogue of detailed morphology classifications for 248,088
galaxies within the BMz survey, accompanied by usage recommendations.
