UniverseTBD/mmu_gaia_gaia

mmu_gaia_gaia HATS目录集合是一个基于欧洲空间局（ESA）Gaia任务第三次数据发布（DR3）的天文数据集，专门包含约2.2亿颗拥有BP/RP（蓝光/红光光度计）光谱的恒星，而非完整的近20亿个源。该数据集是多模态的，整合了恒星光谱、天体测量数据（如位置、视差、自行运动）、测光数据（星等和通量）、通过测光估计的恒星物理参数（如距离、表面重力、金属丰度、表面温度）以及径向速度，同时包含相关的不确定度和质量标志。数据集采用HATS（分层天文表存储）格式，以Apache Parquet数据集形式组织，支持通过LSDB Python框架高效访问和交叉匹配。典型应用包括银河系科学研究（如识别银河盘的非对称特征、构建化学动力学图）和机器学习任务（如生成恒星光谱、估计物理参数、填补缺失光谱区域）。用户使用时需注明引用Gaia任务及数据处理与分析联盟（DPAC）。

The mmu_gaia_gaia HATS Catalog Collection is an astronomical dataset based on the European Space Agency (ESA) Gaia mission Data Release 3 (DR3), specifically containing approximately 220 million stars with BP/RP (Blue Photometer/Red Photometer) spectra, rather than the full set of nearly 2 billion sources. It is multimodal, integrating stellar spectra, astrometric measurements (such as positions, parallaxes, and proper motions), photometry (magnitudes and fluxes), photometrically-estimated stellar parameters (e.g., distance, surface gravity, metallicity, surface temperature), and radial velocities, along with associated uncertainties and quality flags. The dataset is stored in the HATS (Hierarchical Astronomical Table Storage) format, organized as Apache Parquet datasets, and supports efficient access and cross-matching via the LSDB Python framework. Typical use cases include Milky Way science research (e.g., identifying non-axisymmetric features in the Galactic disc, constructing chemodynamical maps) and machine learning applications (e.g., generating stellar spectra, estimating physical parameters, inpainting missing spectral regions). Users should acknowledge the Gaia mission and the Gaia Data Processing and Analysis Consortium (DPAC) when using the data.