arXiv 2407.034092024-07-03astro-ph.GA

从光晕到星系。IX. SDSS星系群光晕组装历史的估算

From Halos to Galaxies. IX. Estimate of Halo Assembly History for SDSS Galaxy Groups

Cheqiu Lyu, Yingjie Peng, Yipeng Jing, Xiaohu Yang, Luis C. Ho, Alvio Renzini, Dingyi Zhao, Filippo Mannucci, Houjun Mo, Kai Wang, Bitao Wang, Bingxiao Xu, Jing… 展开作者

Cheqiu Lyu, Yingjie Peng, Yipeng Jing, Xiaohu Yang, Luis C. Ho, Alvio Renzini, Dingyi Zhao, Filippo Mannucci, Houjun Mo, Kai Wang, Bitao Wang, Bingxiao Xu, Jing Dou, Anna R. Gallazzi, Qiusheng Gu, Roberto Maiolino, Enci Wang, Feng Yuan

备注：: 18 pages, 7 figures. Accepted by ApJ

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AI总结：

本文利用半解析模拟和机器学习模型，分别针对蓝色和红色星系群，仅凭可观测量首次估算了SDSS星系群的光晕组装时间，精度约1.09 Gyr，为观测中研究光晕与星系关联提供了新手段。

摘要：

星系的性质与其宿主光晕质量和光晕组装历史密切相关。在观测中准确测量光晕组装历史具有挑战性，但对于理解星系的形成与演化至关重要。中心星系的恒星与光晕质量比（$M_*/M_{\mathrm{h}}$）常被用来指示星系群的光晕组装时间$t_{\mathrm{h,50}}$，其中$t_{\mathrm{h,50}}$是光晕组装其当前维里质量一半时的回溯时间。利用来自半解析模型的模拟数据，我们发现$M_*/M_{\mathrm{h}}$与$t_{\mathrm{h,50}}$之间存在显著弥散，并且具有恒星形成中心星系（蓝色群）和被动中心星系（红色群）的星系群之间存在很强的系统性差异。为了提高精度，我们开发了机器学习模型，仅使用模拟中的可观测量来估算星系群的$t_{\mathrm{h,50}}$。由于恒星形成淬灭会使暗物质与重子物质的共同增长解耦，我们分别对蓝色群和红色群训练模型。我们的模型成功恢复了$t_{\mathrm{h,50}}$，精度约为1.09 Gyr。通过对模拟中各个可观测量与SDSS观测进行仔细校准，我们将训练好的模型应用于SDSS Yang等人星系群，并首次推导出每个星系群的$t_{\mathrm{h,50}}$。推导出的SDSS $t_{\mathrm{h,50}}$分布与模拟中的分布吻合良好，尤其是对于蓝色群。推导出的光晕组装历史，连同光晕质量，为在观测中研究光晕与星系的关联迈出了重要一步。

英文摘要：

The properties of the galaxies are tightly connected to their host halo mass and halo assembly history. Accurate measurement of the halo assembly history in observation is challenging but crucial to the understanding of galaxy formation and evolution. The stellar-to-halo mass ratio ($M_*/M_{\mathrm{h}}$) for the centrals has often been used to indicate the halo assembly time $t_{\mathrm{h,50}}$ of the group, where $t_{\mathrm{h,50}}$ is the lookback time at which a halo has assembled half of its present-day virial mass. Using mock data from the semi-analytic models, we find that $M_*/M_{\mathrm{h}}$ shows a significant scatter with $t_{\mathrm{h,50}}$, with a strong systematic difference between the group with a star-forming central (blue group) and passive central (red group). To improve the accuracy, we develop machine-learning models to estimate $t_{\mathrm{h,50}}$ for galaxy groups using only observable quantities in the mocks. Since star-formation quenching will decouple the co-growth of the dark matter and baryon, we train our models separately for blue and red groups. Our models have successfully recovered $t_{\mathrm{h,50}}$, within an accuracy of $\sim$ 1.09 Gyr. With careful calibrations of individual observable quantities in the mocks with SDSS observations, we apply the trained models to the SDSS Yang et al. groups and derive the $t_{\mathrm{h,50}}$ for each group for the first time. The derived SDSS $t_{\mathrm{h,50}}$ distributions are in good agreement with that in the mocks, in particular for blue groups. The derived halo assembly history, together with the halo mass, make an important step forward in studying the halo-galaxy connections in observation.

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