发表机构
University of Kansas; Siena University; Union College; INAF- Osservatorio astronomico di Padova; Steward Observatory, University of Arizona; INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna; Observatoire de Paris, LUX, Collège de France, CNRS, PSL University, Sorbonne University; INAF – Osservatorio Astronomico di Trieste; Dipartimento di Fisica G. Occhialini, Università degli Studi di Milano-Bicocca; Laboratoire d’Astrophysique, École Polytechnique Fédérale de Lausanne (EPFL)（堪萨斯大学; 锡耶纳大学; 联合学院; 意大利国家天体物理研究所帕多瓦天文台; 亚利桑那大学斯图尔德天文台; 意大利国家天体物理研究所博洛尼亚天体物理学与空间科学观测站; 巴黎天文台，法国学院，法国国家科学研究中心，巴黎文理研究大学，索邦大学; 意大利国家天体物理研究所的里雅斯特天文台; 米兰比可卡大学G. Occhialini物理系; 洛桑联邦理工学院天体物理实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究用k-means聚类将2831个室女座星系分为三类，发现环境淬灭的有效性取决于星系结构类别，为天文学大数据研究提供了实用方法。
AI 中文摘要
星系演化领域长期面临的一个挑战是，如何从众多相互关联的属性中梳理出独立的星系环境对其恒星形成历史的影响。为解决这一多维问题，我们对室女座星系团延伸区域内的2831个星系应用k-means聚类算法，以定义结构相似星系的客观、可重复子集。利用尺寸、光分布和恒星质量的测量值，k-means将这些星系划分为三类特征组（FCs）：矮星系、椭球星系和大型盘星系。这些特征组不仅结构不同，且与恒星形成主序列的偏移量达到了3σ以上的显著性水平，其中椭球星系群体系统性地向更低的恒星形成率偏移。在每个特征组内研究环境依赖性时，我们发现更致密的环境与更强的淬灭作用相关联。不过，矮星系和大型盘星系的恒星形成直到进入富星系群和星系团环境才会受到显著影响；而椭球星系的恒星形成则随环境密度增加而平滑下降。我们验证了这些趋势并非由每个环境内Sersic指数的差异所驱动，表明环境淬灭的有效性取决于星系的结构类别。我们的结果证明，采用简单的机器学习模型，基于少量参数创建结构相似星系的宽泛类别具有实用性，这对在天文学数据丰富的时代开展研究具有重要意义。
英文摘要
A persistent challenge in galaxy evolution involves disentangling the many correlated properties in order to isolate the effects of a galaxy's environment on its star formation history. To address this multidimensionality problem, we apply k-means clustering to 2831 galaxies in the extended regions around the Virgo cluster to define objective, reproducible subsets of structurally similar galaxies. Using measurements of size, light distribution, and stellar mass, k-means partitions these galaxies into three feature classes (FCs): dwarfs, spheroids, and large disks. In addition to being structurally different, these FCs show distinct offsets from the star-forming main sequence to $>3σ$ significance, with the spheroid population systematically shifted to lower star formation rates. Examining environmental dependence within each FC, we find that denser environments are associated with progressively stronger quenching. However, star formation for the dwarf and large disk galaxies is not strongly affected until the rich group and cluster environments. For the spheroid galaxies, star formation instead smoothly decreases as environment density increases. We verify that these trends are not driven by differences in Sersic index within each environment, suggesting that the effectiveness of environmental quenching depends on the galaxy's structural class. Our results speak to the utility of a simple machine learning model to create broad classes of structurally similar galaxies based on a small set of parameters, which has important implications for navigating this data rich era of astronomy.
Comments26 pages, 12 figures. Submitted to ApJ
