MUSHY：用于光谱数据融合的多模态流式摊销贝叶斯推断
MUSHY: Multimodal Flow-Based Amortized Bayesian Inference for Spectroscopic Data Fusion
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- Princeton University（普林斯顿大学）
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中文总结 AI 辅助
提出MUSHY方法，利用多模态整流流模型进行摊销贝叶斯推断，融合不同巡天光谱数据以精确推断恒星物理参数，并验证多模态输入能提供更强约束。
中文摘要 AI 辅助
我们正处于光谱数据前所未有的可用性时代，诸如APOGEE、GALAH和DESI等巡天项目观测了数百万颗恒星。然而，数据是异构的，迄今为止，利用巡天之间协同效应的努力十分有限。特别是，缺乏将不同巡天的光谱数据结合起来以改进物理参数推断的严谨方法。在这项工作中，我们提出了一种新的光谱数据融合方法MUSHY。MUSHY是一种摊销贝叶斯推断方法，使用具有多模态输入的整流流模型来学习物理参数的后验分布。利用通过ATLAS12/SYNTHE生成的合成光谱，包括真实的仪器效应和噪声，我们证明了MUSHY能够从任何巡天的组合中精确推断物理参数，并获得校准良好的后验分布，多模态输入比单一巡天数据单独提供更强的后验约束。这项工作为从异构来源进行严谨的光谱数据组合铺平了道路。
英文摘要
We are in an era of unprecedented spectroscopic data availability, with surveys such as APOGEE, GALAH, and DESI observing millions of stars. However, data are heterogeneous and to date, efforts to exploit synergies between surveys have been limited. In particular, there is a lack of principled methods that combine spectroscopic data from different surveys to improve inference of physical parameters. In this work we present a new method, MUSHY, for spectroscopic data fusion. MUSHY is an amortized Bayesian inference method, using a rectified flow model with multimodal inputs to learn a posterior distribution over physical parameters. Using synthetic spectra generated via ATLAS12/SYNTHE, including realistic instrumental effects and noise, we demonstrate that MUSHY can precisely infer physical parameters with well-calibrated posteriors from any combination of surveys, with multimodal inputs providing stronger constraints on the posterior than single-survey data alone. This work paves the way for principled combination of spectroscopic data from heterogeneous sources.
