发表机构
University of Michigan; Michigan Institute for Data and AI in Society, University of Michigan; Case Western Reserve University; University of Auckland; The University of Arizona（密歇根大学; 密歇根大学数据与人工智能社会研究所; 凯斯西储大学; 奥克兰大学; 亚利桑那大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究构建了TNOs的光学-近红外光谱流形，将光学颜色视为其压缩表示，通过该流形实现颜色与光谱的双向映射，为TNOs群体成分研究提供了新方法。
AI 中文摘要
长期以来，宽带光学颜色被公认为是海王星外天体（TNOs）表面光谱特性的示踪剂，但其与近红外（NIR）光谱的关系尚未得到定量表征。我们基于詹姆斯·韦布空间望远镜（JWST）对40个TNOs和8个特洛伊小行星的光谱数据构建了经验性的光学-近红外光谱流形，并将宽带测光解释为该流形的压缩表示。利用光谱流形作为先验，模型以观测到的宽带光学颜色为条件，推断光谱潜在空间中的后验分布。随后我们通过后验熵和Kullback-Leibler信息增益评估滤光片组合，发现g、r滤光片在流形上提供了广泛的定位，而额外的滤光片可大幅降低后验不确定性。独立定义的光学颜色群体在光谱流形的主要成分类别上实现了连贯映射，为先前识别的颜色群体提供了物理解释。所学的流形还支持逆映射回光学颜色空间，对该流形进行采样和解码可重现观测到的光学颜色空间拓扑结构，表明这些颜色群体是连续光学-近红外成分流形的低维投影，而非孤立的经验聚类。正向和逆映射将光学颜色统一解释为光学-近红外光谱流形的压缩表示。随着JWST扩展该流形且鲁宾望远镜（Rubin）将数百万个天体映射其上，该框架为群体规模的成分研究和稀有表面类型的发现提供了创新方法。
英文摘要
Broadband optical colors have long been recognized as tracers of the surface spectral properties of trans-Neptunian objects (TNOs), yet their relationship to near-infrared (NIR) spectra has not been quantitatively characterized. We construct an empirical optical-NIR spectral manifold from JWST spectroscopy of 40 TNOs and 8 Neptune Trojans and interpret broadband photometry as compressed representations of this manifold. Using the spectral manifold as a prior, the model conditions on observed broadband optical colors to infer a posterior distribution in the spectral latent space. We then evaluate filter combinations through posterior entropy and Kullback-Leibler information gain and found that the gr colors provide broad localization on the manifold, while additional filters substantially reduce posterior uncertainty. Independently defined optical color populations map coherently onto the major compositional classes of the spectral manifold, providing a physical interpretation of previously identified color groups. The learned manifold also enables the inverse mapping back to optical color space. Sampling and decoding the manifold reproduces the observed topology of optical color space, indicating that these color populations arise naturally as low-dimensional projections of a continuous optical-NIR compositional manifold rather than isolated empirical clusters. The forward and inverse mappings provide a unified interpretation of optical colors as compressed representations of an optical-NIR spectral manifold. As JWST expands the manifold and Rubin maps millions of objects onto it, this framework provides an innovated approach to population-scale compositional studies and the discovery of rare surface types.
Commentsv2 with some bugs fixed. 19 pages, 10 figures
