AI 中文总结
该研究旨在识别红移2.5<z<5的大量静止星系，通过基于圣克鲁斯半解析模型模拟测光数据训练CatBoostClassifier，将其应用于COSMOS2025样本，发现该模型识别的静止候选星系更多，指出SED拟合方法可能遗漏关键过渡阶段静止星系，分类器和样本可公开获取。
AI 中文摘要
高红移（z≳2）下大量静止星系的存在强烈限制了星系演化模型中的快速淬火机制。我们提出了一个机器学习框架，用于在COSMOS2025目录中识别红移在2.5<z<5且质量大（log(M*/M⊙)>9.5）的静止星系。我们使用圣克鲁斯半解析模型（SAMs）的模拟测光数据训练了一个CatBoostClassifier，纳入关键的JWST NIRCam波段和实际噪声，将基于特定恒星形成率的SAM衍生静止标签转移到观测空间。与SAM真实情况验证时，我们的分类器召回率（完整性）显著更高，达到78%（光谱能量分布（SED）拟合为53%），同时保持82%的高纯度。应用于COSMOS2025样本，假设SAM静止定义适用于真实宇宙，该模型识别出1111个静止候选星系，比通过目录简单SED拟合配置识别的427个候选星系多2.6倍。在SAM静止定义下，这种高纯度但低完整性的一致模式表明，受简化参数化恒星形成历史约束的SED拟合方法可能遗漏了很大一部分静止星系群体，可能是处于关键过渡演化阶段的星系。训练好的分类器和分类后的COSMOS2025样本可公开获取。
英文摘要
The existence of massive quiescent galaxies at high redshifts ($ \text{z} \gtrsim 2$) strongly constrains the rapid quenching mechanisms in galaxy evolution models. We present a machine learning framework to identify massive ($\log(\text{M}_*/\text{M}_\odot) > 9.5$) quiescent galaxies at $2.5 < \text{z} < 5$ in the COSMOS2025 catalog. We train a \texttt{CatBoostClassifier} on mock photometry from the Santa Cruz semi-analytic models (SAMs), incorporating key JWST NIRCam bands and realistic noise to transfer the SAM-derived quiescent label (based on specific star-formation rate) to the observational space. When validated against the SAM ground truth, our classifier achieves a significantly higher recall (completeness) of 78\% (compared to 53\% for spectral energy distribution (SED)-fitting), while maintaining a high purity of 82\%. Applied to the COSMOS2025 sample, and assuming the SAM definition of quiescence transfers to the real Universe, the model identifies 1111 quiescent candidates, a population 2.6 times larger than the 427 candidates identified via the catalog's simple SED-fitting configuration. Under the SAM definition of quiescence, this consistent pattern of high purity but poor completeness suggests that the SED-fitting methods, constrained by simplified parametric star-formation histories, may miss a significant fraction of the quiescent population, likely galaxies in crucial transitional evolutionary stages. The trained classifier and classified COSMOS2025 sample are publicly available.
Comments17 pages, 12 figures, 6 tables, Accepted for Publication in ApJ
Journal refThe Astrophysical Journal, 1007:60 (14pp), 2026 August 10
