星系形态分类中的不确定性估计
Estimating Uncertainty in Galaxy Morphology Classification
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中文总结 AI 辅助
针对星系形态分类（GMC）中基础模型无法量化不确定性的问题，提出后验框架UEGMC，可从冻结骨干网络提取的表示中直接预测不确定性，实验显示其不确定性量化性能具竞争力。
中文摘要 AI 辅助
天文学家对星系形态进行分类以研究宇宙演化。尽管深度基础模型越来越多地被用于星系形态分类（Galaxy Morphology Classification, GMC），但针对GMC结果的不确定性评估研究较少。不确定性评估十分重要，因为天文数据受仪器和环境限制存在固有噪声，且星系的持续演化会产生内在形态歧义。然而，当前基础模型作为确定性点估计器运行，无法量化不确定性。为克服这一局限，我们提出UEGMC，这是一种用于星系形态分类的后验不确定性估计框架。它通过模型参数、天文数据、参考标准或内在物理歧义，将GMC中的不确定性分为不同类型，从而促进更优的分类。该框架可直接从基础模型冻结骨干网络提取的表示中预测不确定性，无需计算成本高昂的采样，因此支持细粒度的不确定性评估。实验结果表明，UEGMC与现有方法相比具有竞争力的不确定性量化性能。
英文摘要
Astronomers classify galaxy morphology to investigate cosmic evolution. While deep foundation models are increasingly utilized in Galaxy Morphology Classification (GMC), little work has been done on evaluating the uncertainty of GMC results. Uncertainty evaluation is important because astronomical data are inherently noisy due to instrumental and environmental limitations. Also, the continuous evolution of galaxies creates intrinsic morphological ambiguity. However, current foundation models operate as deterministic point estimators, failing to quantify the uncertainty. To overcome this limitation, we propose UEGMC, a post-hoc framework of Uncertainty Estimation for Galaxy Morphology Classification. It categorizes uncertainty in GMC into distinct types by model parameters, astronomical data, reference standards, or intrinsic physical ambiguities, thereby facilitating better classification. Our framework can directly predict uncertainties from representations extracted from the frozen backbones of foundation models, without computationally expensive sampling, therefore enabling fine-grained uncertainty evaluations. Our experimental results demonstrate that UEGMC provides competitive uncertainty quantification performance compared with previous methods.
