学习Gaia：基于Gaia数据发布3源目录训练的生成模型
Learning Gaia: A Generative Model Trained on the Gaia Data Release 3 Source Catalog
- Space Telescope Science Institute（太空望远镜科学研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文训练条件流匹配模型生成模拟Gaia源目录，可填补测光与运动学信息，重现银河系盘与麦哲伦云，为未来基础模型奠定基础。
AI中文摘要：
我在Gaia数据发布3源目录中约5.79亿个源的位置、测光和天体测量数据上训练了一个条件流匹配模型。通过在训练过程中使用概率掩蔽方案，我开发了一个能够生成模拟Gaia目录源以及具有真实Gaia源部分测量值的源的模型。该模型可以生成粗粒度的模拟Gaia源目录，重现银河系盘面和麦哲伦云，并能填补测光和运动学信息。该模型无法重现像ω半人马座这样小而独特的子群。该模型是向基于全部Gaia数据产品训练的基础模型迈出的必要一步，并为未来利用即将到来的Gaia数据发布4开展的工作提供了蓝图。
英文摘要：
I train a conditional flow matching model on the positions, photometry, and astrometry of $\sim$ 579 million sources from the Gaia Data Release 3 Source Catalog. Using a probabilistic masking scheme during training, I develop a model capable of generating mock Gaia catalog sources with and without partial measurements of real Gaia sources. The model can produce coarse-grained mock Gaia Source Catalogs that reproduce the Milky Way disk and Magellanic Clouds, and can impute photometric and kinematic information. The model fails to reproduce small, distinct subpopulations like $ω$ Centauri. This model is a necessary step towards a foundation model trained on the entirety of Gaia data products, and provides a blueprint for future work with the upcoming Gaia Data Release 4.
