《Gaia》DR3 非单星星表中年轻星团内的高优先级致密天体候选体
High-Priority Compact-Object Candidates in Young Clusters from the \textit{Gaia} DR3 Non-Single-Star Catalog
浏览论文内容
中文总结 AI 辅助
通过交叉匹配Gaia星表与非单星数据库并应用新开发的AMRF分类，识别出年轻星团中13个高伴星质量系统，其中3个可能为高质量致密天体候选，为后续观测提供目标。
中文摘要 AI 辅助
在星团中搜寻大质量致密天体，如黑洞（BH）或中子星（NS），是天体物理学中长期存在的挑战，然而至今尚未有在年轻星团中明确识别出此类致密天体的报道。在本封信中，我们构建了一个用于后续研究的高优先级候选体样本。通过将星团星表与Gaia非单星数据库进行交叉匹配，并估算伴星质量，我们识别出13个伴星质量超过1.4倍太阳质量（$M_{\odot}$）的系统，其中三个系统的质量超过2.1倍太阳质量。应用我们新开发的、基于星团特定等时线的天体测量质量比函数（AMRF）分类方法，我们识别出三个系统，其伴星无法轻易用单颗主序星或未分辨的密近双星来解释。这些系统的推断伴星质量超过2.1倍太阳质量，表明它们可能是高质量致密天体候选体。对比较子集进行的联合Gaia和视向速度轨道拟合得出的伴星质量与天体测量估计值大致一致，支持我们框架用于目标优先级排序。这些未确认的系统是多历元光谱、高分辨率成像以及未来Gaia历元天体测量的宝贵目标。
英文摘要
The search for massive compact objects such as black holes (BHs) or neutron stars (NSs) in star clusters is longstanding challenge in astrophysics, yet no definitive identification of such compact objects in young clusters has been reported. In this Letter, we construct a sample of high-priority candidates for follow-up investigation. By cross-matching the cluster catalog with the Gaia Non-Single-Star database and estimating companion masses, we identify 13 systems with companion masses exceeding 1.4~$M_{\odot}$, three of which have masses above 2.1~$M_{\odot}$. Applying our newly developed Astrometric Mass-Ratio Function (AMRF) classification based on cluster-specific isochrones, we identify three systems whose companions cannot readily be explained by a single main-sequence star or an unresolved close binary. These systems have inferred companion masses exceeding 2.1~$M_{\odot}$, suggesting that they may be high-mass compact-object candidates. Joint Gaia and radial velocity orbit fitting for a comparison subset yields companion masses broadly consistent with the astrometric estimates, supporting the use of our framework for target prioritization. These unconfirmed systems are valuable targets for multi-epoch spectroscopy, high-resolution imaging, and future Gaia epoch astrometry.
发表机构
- China Three Gorges University（三峡大学）
- Purple Mountain Observatory, Chinese Academy of Sciences（中国科学院紫金山天文台）
- University of Science and Technology of China（中国科学技术大学）
机构由 AI 辅助整理，请以论文原文为准。
