AI 中文总结
本研究基于星系团模拟训练机器学习模型，可识别反冲星系，对首次坍缩星系的识别精度超80%，应用于室女座星系团后发现具不对称HI分布的星系均为首次接近星系团，相关模型已公开。
AI 中文摘要
星系团外围的星系群中包含两类成员：一类是在星系团的群与纤维结构中经历过预处理的星系，另一类是近期曾穿过星系团中心的反冲星系。然而，从观测上区分这两类星系的演化路径颇具挑战。本研究提出了一种机器学习驱动的模型，该模型基于《The Three Hundred》模拟套件中的星系团模拟数据训练而成，可用于天文观测中识别单个反冲星系。该模型构建的反冲星系样本的纯度和完整度最高可达约70%，首次坍缩星系的纯度和完整度则超过80%；它可通过调整参数优化上述任一指标，且能与任意组合的可观测物理量搭配使用。我们还将该模型应用于室女座星系团中具有不对称HI（中性氢）分布的星系，结果表明这些星系极有可能是首次接近该星系团。这一发现支持了冷气体在星系进入星系团后不久便被剥离的观点，同时证明该分类器能帮助人们更好地理解星系的哪些属性是由其此前穿过星系团的经历所导致的。我们已将该模型以网页应用的形式公开，链接见本文结论部分。
英文摘要
The galaxy population in the outskirts of a cluster contains members that have been pre-processed in groups and filaments, as well as backsplash galaxies -- those that have recently passed through the cluster's center. However, disentangling these two pathways is challenging observationally. In this work, we present a machine-learning-powered model, trained on simulations of galaxy clusters from The Three Hundred suite of simulations, which can identify individual backsplash galaxies in astronomical observations. This model can build samples of backsplash galaxies with a purity and completeness of up to ~70%, and galaxies on their first infall with a purity and completeness of over 80%. It can be tuned to optimise either of these two metrics, and can be used with any combination of a set of observable quantities. We have also applied this model to galaxies with asymmetric HI distributions in the Virgo Cluster, and have demonstrated that these galaxies are all likely approaching the cluster for the first time. This supports the idea that cold gas is removed from these galaxies soon after entering a cluster, and demonstrates how this classifier can provide a better understanding of which properties of galaxies are caused by a previous passage through a cluster. We have made this model publicly available in the form of a web app, with a link in the Conclusions of this paper.
Comments18 pages, 12 figures, 2 tables, submitted to MNRAS; link to associated web app in Conclusions of paper
