arXiv 2412.090402024-12astro-ph.GA

通过Gaia光谱推断巨星级别年龄实现银河考古

Towards Galactic Archaeology with Inferred Ages of Giant Stars From Gaia Spectra

Mullard Space Science Laboratory, University College London（穆拉德空间科学实验室，伦敦大学学院）；National Astronomical Observatory of Japan（日本国立天文台）；Kavli Institute for Particle Astrophysics & Cosmology (KIPAC), Stanford University（卡弗里粒子天体物理与宇宙学研究所（KIPAC），斯坦福大学）；Research School of Astronomy and Astrophysics, Australian National University（天文与天体物理研究学院，澳大利亚国立大学）；School of Computing, Australian National University（计算机学院，澳大利亚国立大学）；Department of Physics and Astronomy, University College London（物理与天文学系，伦敦大学学院）；Dipartimento di Fisica e Astronomia, Universita´ degli Studi di Bologna（物理与天文学系，博洛尼亚大学）；INAF – Osservatorio di Astrofisica e Scienza dello Spazio（意大利国家天体物理研究所-空间天体物理与科学观测站）

Aisha S. Almannaei, Daisuke Kawata, Ioana Ciuca, Connor Fallows, Jason L. Sanders, George Seabroke, Andrea Miglio

备注：: 15 pages, 9 figures. Accepted by MNRAS
DOI：: 10.1093/mnras/staf2252

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AI总结：

利用Gaia光谱数据，通过机器学习推断巨星级别恒星年龄，揭示银河系盘恒星的时间和化学特征，增强对银河系形成和演化的理解。

摘要：

在Gaia时代，恒星年龄的准确确定正在改变银河考古学。我们证明了从Gaia的RVS光谱和BP/RP（XP）光谱摄影数据中推断恒星年龄的可行性，特别是针对红巨星分支和高质量红团星。我们成功训练了两个机器学习模型，称为SIDRA：从Gaia光谱推导出的恒星年龄预测。SIDRA-RVS模型使用RVS光谱，SIDRA-XP模型使用从XP光谱中获得的恒星参数。两种模型都使用APOGEE衍生的恒星年龄作为训练数据。SIDRA-RVS对年龄约为τ_BINGO=10 Gyr的恒星的年龄估计具有残差标准差约为0.12 dex的未见过的测试数据集，而SIDRA-XP在τ_BINGO=10 Gyr附近的恒星中具有更高的精度，残差约为0.064 dex。由于SIDRA-XP优于SIDRA-RVS，我们应用SIDRA-XP来分析2218154颗恒星的年龄。这使我们能够绘制银河盘恒星的时间和化学特性，重现已知的特征，如Gaia-Sausage-Enceladus合并和可能的气体丰富相互作用事件，与第一个Sagittarius矮星系的入侵有关。本研究证明了将机器学习技术应用于Gaia光谱可以提供有价值的单个年龄信息，特别是对于巨星级别恒星，从而增强我们对银河系形成和演化的理解。

英文摘要：

In the era of Gaia, the accurate determination of stellar ages is transforming Galactic archaeology. We demonstrate the feasibility of inferring stellar ages from Gaia's RVS spectra and the BP/RP (XP) spectrophotometric data, specifically for red giant branch and high-mass red clump stars. We successfully train two machine learning models, dubbed SIDRA: Stellar age Inference Derived from Gaia spectRA to predict the age. The SIDRA-RVS model uses the RVS spectra and SIDRA-XP the stellar parameters obtained from the XP spectra. Both models use BINGO, an APOGEE-derived stellar age as the training data. SIDRA-RVS estimates ages of stars whose age is around $τ_\mathrm{BINGO}=10$~Gyr with a standard deviation of residuals of $\sim$ 0.12 dex in the unseen test dataset, while SIDRA-XP achieves higher precision with residuals $\sim$ 0.064 dex for stars around $τ_\mathrm{BINGO}=10$~ Gyr. Since SIDRA-XP outperforms SIDRA-RVS, we apply SIDRA-XP to analyse the ages for 2,218,154 stars. This allowed us to map the chronological and chemical properties of Galactic disc stars, reproducing the known distinct features such as the Gaia-Sausage-Enceladus merger and a potential gas-rich interaction event linked to the first infall of the Sagittarius dwarf galaxy. This study demonstrates that machine learning techniques applied to Gaia's spectra can provide valuable individual age information, particularly for giant stars, thereby enhancing our understanding of the Milky Way's formation and evolution.

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