星系形态分类：恒星圆度是否足够？
Galaxy Morphology Classification: Are Stellar Circularities Enough?
AI总结：
本研究利用IllustrisTNG模拟中的恒星圆度进行星系形态分类，提出F_disk=0.25阈值区分早/晚型星系，并验证了形态-密度关系，证明该方法高效且可支持机器学习。
摘要：
我们提出了一项初步研究，探讨模拟星系中恒星轨道圆度（可从IllustrisTNG项目的预计算目录中获得）是否可以作为广泛形态分类的替代指标。我们重点关注公开的“恒星圆度、角动量、轴比”目录，该目录能够将恒星成分简单运动学分解为盘状和球状子系统。通过将该方法与TNG50中更详细的五成分运动学分解进行验证，我们确认基于圆度的盘分数与薄盘的相关性最强，而核球分数大致代表了经典核球和恒星晕的综合贡献。随后，我们将此分解应用于红移$z=0$的TNG100模拟中的星系，并确定了一个数据驱动的阈值$\rm{F_{disk}} = 0.25$，用以区分早型星系和晚型星系。该阈值低于通常采用的0.4，能更好地捕捉盘主导系统的多样性，并避免排除具有中等明显盘的星系。此外，我们根据总盘和球状质量分数较低的星系，识别出不规则或形态复杂的系统。利用此分类，我们恢复了与观测大致一致的形态-密度关系：晚型星系在场中占主导，而早型星系是星系团中最普遍的形态类型。我们的结果表明，仅凭恒星圆度即可作为一种易于获取且计算高效的形态替代指标。我们还讨论了该分类作为基线或训练集支持机器学习工作的潜力，以用于未来的形态研究。
英文摘要：
We present a preliminary study exploring whether the stellar orbital circularity of simulated galaxies, available from precomputed catalogs in the IllustrisTNG project, can be used as a proxy for broad morphological classification. We focus on the publicly available "Stellar Circularities, Angular Momenta, Axis Ratios" catalog, which enables a simple kinematic decomposition of the stellar component into disk and spheroid subsystems. By validating this approach against the more detailed five-component kinematic decomposition in TNG50, we confirm that the circularity-based disk fraction correlates most strongly with the thin disk, while the bulge fraction broadly represents the combined contribution of classical bulges and stellar halos. We then apply this decomposition to galaxies in the TNG100 simulation at redshift $z=0$ and identify a data-motivated threshold of $\mathrm{F_{disk}} = 0.25$ to distinguish early- and late-type galaxies. This threshold, lower than the commonly adopted value of $0.4$, better captures the diversity of disk-dominated systems and avoids excluding galaxies with moderately prominent disks. Additionally, we identify irregular or morphologically complex systems based on galaxies with low total disk and spheroid mass fractions. Using this classification, we recover a morphology-density relation that is broadly consistent with observations: late-type galaxies dominate in the field, while early-type galaxies are the most prevalent morphological type in clusters. Our results demonstrate that stellar circularity alone can serve as an accessible and computationally efficient morphological proxy. We also discuss the potential for this classification to support machine learning efforts as a baseline or training set for future morphological studies.
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