用无监督域适应让星系分类模型跨数据集通用，提升新观测数据的分析精度。
From Galaxy Zoo DECaLS to BASS/MzLS: detailed galaxy morphology classification with unsupervised domain adaption
- 通过无监督域适应迁移训练好的星系分类模型，适配不同观测条件下的图像。
- 在BMz数据上，微调后模型性能显著优于直接使用原模型，接近源数据表现。
- 公开24.8万星系的详细分类结果，适合天体物理与深度学习交叉研究者使用。
DESI遗产成像巡天（DESI-LIS）包含三个独立巡天：暗能量相机遗产巡天（DECaLS）、北京-亚利桑那天空巡天（BASS）和梅纳尔z波段遗产巡天（MzLS）。公民科学项目银河动物园DECaLS第5版（GZD-5）为DECaLS中253,287个星系提供了详尽的形态标签，成为众多基于深度学习的星系形态分类研究的基础。然而，由于DECaLS与BASS/MzLS（统称BMz）在信噪比和分辨率上的差异，仅在DECaLS上训练的神经网络无法直接应用于BMz图像，存在分布偏移问题。本研究探索一种无监督域适应（UDA）方法，将基于GZD-5标签在DECaLS上训练的源域模型，通过微调适配至BMz图像，以降低BMz星系分类中的偏差。源域模型在DECaLS验证集上表现与已有工作相当；在BMz上，经微调的目标域模型性能显著优于直接应用源模型，达到与源域相近水平。同时，本文发布了对BMz巡天中248,088个星系的详细形态分类目录，并附使用建议。
原文摘要 · Abstract (English)
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.
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