利用人工智能对邻近星系原位与外部吸积恒星质量的约束
Constraints on the in-situ and ex-situ stellar masses in nearby galaxies with Artificial Intelligence
AI总结：
该研究利用基于TNG50模拟训练的机器学习模型分析MaNGA数据，揭示邻近星系中原位恒星质量普遍占主导，而外部吸积质量仅在大于1e11倍太阳质量的星系中显著，且与椭圆、淬灭、慢速旋转及大质量暗晕中心等特性密切相关。
摘要：
星系演化的层级模型表明，并合对驱动星系内恒星聚集的复杂过程具有重大影响。然而，准确测量吸积对星系总恒星质量的贡献及其与原位恒星形成之间的平衡，仍然是一个持续的挑战，因为它既无法直接观测，也不容易从观测性质中推断出来。在此，我们利用MaNGA的数据，在一个统计上具有显著意义的邻近星系样本中，提出了基于理论驱动的对并合起源恒星质量比例的预测。我们采用一个稳健的机器学习模型，该模型在模拟的MaNGA类似体（MaNGIA）上进行训练，而这些类似体又来自宇宙学模拟（TNG50）。我们揭示，原位恒星质量几乎在整个恒星质量谱（1e9Msun < M* < 1e12Msun）中占主导地位。只有在质量更大的星系（M* > 1e11Msun）中，吸积质量才成为重要贡献者，最高可达总恒星质量的35-40%。值得注意的是，邻近宇宙中的外部吸积恒星质量表现出对星系特性的显著依赖性，椭圆星系、淬灭星系和慢速旋转体，以及位于更大质量暗物质晕中心的星系，更倾向于具有更高的吸积比例。
英文摘要：
The hierarchical model of galaxy evolution suggests that the impact of mergers is substantial on the intricate processes that drive stellar assembly within a galaxy. However, accurately measuring the contribution of accretion to a galaxy's total stellar mass and its balance with in-situ star formation poses a persistent challenge, as it is neither directly observable nor easily inferred from observational properties. Here, we present theory-motivated predictions for the fraction of stellar mass originating from mergers in a statistically significant sample of nearby galaxies, using data from MaNGA. Employing a robust machine learning model trained on mock MaNGA analogs (MaNGIA) in turn obtained from a cosmological simulation (TNG50), we unveil that in-situ stellar mass dominates almost across the entire stellar mass spectrum (1e9Msun < M* < 1e12Msun). Only in more massive galaxies (M* > 1e11Msun) does accreted mass become a substantial contributor, reaching up to 35-40% of the total stellar mass. Notably, the ex-situ stellar mass in the nearby universe exhibits significant dependence on galaxy characteristics, with higher accreted fractions favored by elliptical, quenched galaxies and slow rotators, as well as galaxies at the center of more massive dark matter halos.
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