学习JWST. I. 面向JADES巡天中新群体发现与形态感知测光红移测量的基础模型
Learning JWST. I. A Foundation Model for New Population Discoveries and Morphology-Aware Photometric Redshift Measurements in the JADES Survey
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中文总结 AI 辅助
本文提出自监督基础模型FM-JADES-v1，用于JWST深场数据，通过盲发现罕见天体（如高红移星系和LRDs）及提升少波段测光红移精度（σ_NMAD=0.157 vs 0.44），展示了多模态表示在大型巡天中的发现与测量潜力。
中文摘要 AI 辅助
我们提出了FM-JADES-v1，一个用于詹姆斯·韦伯太空望远镜（JWST）深场科学的自监督基础模型，该模型使用来自JWST先进深场河外巡天（JADES）第五次数据发布的482,444个天体，基于多波段成像和测光星表进行训练。共享嵌入空间的训练不使用类别标签。我们通过两个实验，即盲主动发现和少波段测光红移，证明了FM-JADES-v1可以作为天体发现和改进性质测量的强大工具。对于盲天体发现，FM-JADES-v1在没有任何先验群体标签或群体特定选择标准的情况下，识别出罕见天体群体，如高红移星系和小红点（LRDs）。这些罕见群体在嵌入空间中表现为孤立的岛屿，可以在没有先验天体物理知识的情况下被识别。对于少波段测光红移，FM-JADES-v1学习到的嵌入在严格控制的三波段（F115W/F200W/F356W）测光红移基准上实现了σ_NMAD=0.157，而模板拟合的σ_NMAD=0.44。这些结果证明了自监督多模态表示作为大型天文巡天可扩展发现空间的潜力。应用于正在进行和未来的JWST、Roman、Euclid和Rubin/LSST广域巡天，该框架可以实现对罕见群体的系统性搜索，并支持多个下游任务，如改进天体物理性质测量。
英文摘要
We present FM-JADES-v1, a self-supervised foundation model for James Webb Space Telescope ({\em JWST}) deep-field science, trained with 482,444 objects from the {\em JWST} Advanced Deep Extragalactic Survey (JADES) Data Release 5 using multi-band imaging and the photometric catalog. The shared embedding space is trained without class labels. We demonstrate that FM-JADES-v1 can serve as a powerful tool for object discovery and improving property measurements using two experiments, blind active discovery and few-band photometric redshift. For blind object discovery, FM-JADES-v1 identifies rare object populations such as high-redshift galaxies and Little Red Dots (LRDs) without any prior population labels or population-specific selection criteria. These rare populations emerge as isolated islands in the embedding space, which can be identified without prior astrophysical knowledge. For few-band photometric redshift, FM-JADES-v1's learned embeddings achieve $σ_{\rm NMAD}=0.157$ in a strictly controlled three-band (F115W/F200W/F356W) photo-$z$ benchmark, compared to $σ_{\rm NMAD}= 0.44$ for template fitting. These results demonstrate the potential of self-supervised multi-modal representations as scalable discovery spaces for large astronomical surveys. Applied to ongoing and future wide-field surveys from JWST, Roman, Euclid, and Rubin/LSST, this framework could enable systematic searches for rare populations, as well as enabling multiple downstream tasks such as improving astrophysical property measurements.
发表机构
- University of Arizona（亚利桑那大学）
- University of Wyoming（怀俄明大学）
机构由 AI 辅助整理，请以论文原文为准。
