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arXiv 2302.06995astro-ph.GAastro-ph.EPastro-ph.IMastro-ph.SRcs.LG
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> 3亿颗盖亚恒星的参数：贝叶斯推断与机器学习

Parameters for > 300 million Gaia stars: Bayesian inference vs. machine learning

Universitat de Barcelona（巴塞罗那大学）；Leibniz-Institut für Astrophysik Potsdam (AIP)（莱布尼茨波茨坦天体物理研究所（AIP））；Universität Potsdam（波茨坦大学）

F. Anders, A. Khalatyan, A. B. A. Queiroz, S. Nepal, C. Chiappini

备注：
To appear in Highlights of Spanish Astrophysics XI, Proceedings of the XV Scientific Meeting of the Spanish Astronomical Society held on September 4 - 9, 2022, in La Laguna, Spain
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摘要：

盖亚数据发布3（DR 3）于2022年6月发布，提供了超过10亿颗恒星的各种天体测量，光度测量和光谱测量。数据的丰富性和复杂性使得传统的估计整个盖亚数据集恒星参数的方法几乎令人望而却步。我们已经探索了不同的监督学习方法，用于提取基本的恒星参数以及距离和视线距离，给定光谱-照片-天体测量数据（也包括新的Gaia XP光谱）。为了训练，我们使用了一个增强的高质量数据集，该数据集是从盖亚DR 3和地面光谱测量数据中编译出来的，覆盖了整个天空和所有银河系的组成部分。我们表明，即使有一个简单的神经网络架构或基于树的算法（在没有盖亚XP光谱），我们成功地预测竞争的结果（相比贝叶斯等时线拟合）到微弱的幅度。我们将在不久的将来提出一个新的盖亚DR 3恒星参数目录，使用目前性能最好的表格数据机器学习算法XGBoost获得。

英文摘要：

The Gaia Data Release 3 (DR3), published in June 2022, delivers a diverse set of astrometric, photometric, and spectroscopic measurements for more than a billion stars. The wealth and complexity of the data makes traditional approaches for estimating stellar parameters for the full Gaia dataset almost prohibitive. We have explored different supervised learning methods for extracting basic stellar parameters as well as distances and line-of-sight extinctions, given spectro-photo-astrometric data (including also the new Gaia XP spectra). For training we use an enhanced high-quality dataset compiled from Gaia DR3 and ground-based spectroscopic survey data covering the whole sky and all Galactic components. We show that even with a simple neural-network architecture or tree-based algorithm (and in the absence of Gaia XP spectra), we succeed in predicting competitive results (compared to Bayesian isochrone fitting) down to faint magnitudes. We will present a new Gaia DR3 stellar-parameter catalogue obtained using the currently best-performing machine-learning algorithm for tabular data, XGBoost, in the near future.

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