发表机构
Instituto de Astronomía Teórica y Experimental, CONICET-UNC; Observatorio Astronómico, Universidad Nacional de Córdoba; Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba（国家科学技术研究委员会-科尔多瓦国立大学理论与实验天文学研究所; 科尔多瓦国立大学天文台; 科尔多瓦国立大学数学、天文学、物理学与计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文发布了用于星系轨道分类的roger v2.0代码，新增宿主晕质量输入，扩展适用质量下限，提供Python实现，可稳健分类星系样本并支持定制训练。
AI 中文摘要
本文介绍了roger代码的更新版本，名为roger v2.0，该代码用于对星系群和星系团内部及周围的星系进行轨道分类。除了投影相空间坐标外，新版本还将宿主晕质量作为额外输入参数。尽管加入晕质量仅会导致分类结果出现适度变化，但有助于提升该方法的整体稳健性。我们还扩展了roger可应用的宿主晕质量范围，使其能分析质量低至10^13.5 h^-1 M⊙的系统。我们进一步提供了新代码的Python实现，该实现将公开可用，使用户能通过任意版本的方法高效且稳健地对任意星系样本进行分类，还允许用户在替代训练集上训练定制分类器，为该方法适配不同数据集和科学应用提供了灵活性。
英文摘要
In this paper, we present an updated version of the roger code, called roger v2.0, developed to perform the orbital classification of galaxies residing in and around galaxy groups and clusters. In addition to the projected phase-space coordinates, the new version incorporates the host halo mass as an additional input parameter. Although the inclusion of the halo mass leads to only modest changes in the classification, it contributes to improving the overall robustness of the method. We also extend the range of host halo masses over which roger can be applied, enabling the analysis of systems with masses down to $10^{13.5} h^{-1} M_{\odot}$. We further provide a Python implementation of the new code, which will be made publicly available. This implementation enables users to efficiently and robustly classify arbitrary galaxy samples using either version of the method. Moreover, it allows users to train a customized classifier on an alternative training set, providing the flexibility to adapt the method to different datasets and scientific applications.
CommentsCode hosted at https://github.com/Martindelosrios/pyROGER. Accepted for its publication at MNRAS
