通用视觉语言模型（VLM）可指导天文基础模型更好地识别星系形态
A General-Purpose VLM Can Teach an Astronomy Foundation Model to Better Recognize Galaxy Morphology
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中文总结 AI 辅助
该研究提出用通用视觉语言模型（VLM）作为弱监督教师，指导天文基础模型Zoobot提升星系形态识别性能，可高效适配未来大型天文巡天任务。
中文摘要 AI 辅助
现有天文基础模型能提供强大的星系表征，但将其适配新巡天条件及巡天特定的形态识别任务仍需大量人工监督。本文表明，基于VLM的视觉问答（VQA）系统蕴含有意义的视觉语义先验，可作为下游形态分类器的弱监督，在有限人工标注预算下提升形态分类效果。我们首先引入覆盖两类代表性成像 regime 的巡天导向VQA基准，并评估当前最优VLM在星系形态问题上的表现；结果显示，这些模型能捕获有用的形态信号与有效不确定性，但可靠性不足以替代人类标注者。基于该发现，我们将通用VLM作为天文基础模型Zoobot的形态教师，Zoobot是在大规模Galaxy Zoo标注上预训练的模型。在两个巡天领域及多种标注预算下，VLM教师均能持续提升Zoobot的下游形态分类性能。这些结果证明，通用VLM提供了与天文基础模型互补的知识，可在有限人工监督下指导其更好地识别星系形态；所构建的流程旨在高效适配即将开展的大型巡天，包括维拉·C·鲁宾天文台的空间和时间遗产巡天（LSST）及南希·格蕾丝·罗曼太空望远镜项目。该基准与代码可在指定URL公开获取。
英文摘要
Existing astronomy foundation models provide strong galaxy representations, but adapting them to new survey conditions and survey-specific morphology recognition tasks still requires substantial human supervision. We show that VLM-based VQA systems contain meaningful visual-semantic priors that can serve as weak supervision for downstream morphology classifiers and improve morphology classification under limited human-label budgets. We first introduce a survey-oriented VQA benchmark spanning two representative imaging regimes and evaluate state-of-the-art VLMs on galaxy morphology questions. The results show that these models capture useful morphology signals and informative uncertainty, but are not sufficiently reliable to replace human annotators. Motivated by this finding, we use a general-purpose VLM as a morphology teacher for Zoobot, an astronomy foundation model pretrained on large-scale Galaxy Zoo annotations. Across two survey domains and multiple annotation budgets, the VLM teacher consistently improves Zoobot's downstream morphology classification. These results demonstrate that a general-purpose VLM provides knowledge complementary to an astronomy foundation model and can teach it to better recognize galaxy morphology under limited human supervision. The resulting pipeline is designed for label-efficient adaptation to forthcoming large-scale surveys, including the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) and the Nancy Grace Roman Space Telescope. The benchmark and code are publicly available at https://github.com/fw-ic/VLM-morphology-teacher.
发表机构
- Stony Brook University（石溪大学）
- University of Technology Sydney（悉尼科技大学）
- Futurewei Technologies（华为主机技术公司）
机构由 AI 辅助整理，请以论文原文为准。
