[论文解读] Improving the open cluster census. I. Comparison of clustering algorithms applied to Gaia DR2 data

Emily L. Hunt, S. Reffert|arXiv (Cornell University)|Dec 8, 2020

Stellar, planetary, and galactic studies参考文献 48被引用 4

一句话总结

本研究比较了DBSCAN、HDBSCAN和高斯混合模型（GMMs）在Gaia DR2数据中检测疏散星团的性能，发现HDBSCAN因具备更高的灵敏度（最高达82%）以及在不同距离和密度下恢复星团的能力而表现最优，尽管需要后处理以减少误报。研究共识别出41个新的疏散星团候选体，其中三个位于500 pc以内，表明银河系的疏散星团普查仍不完整。

ABSTRACT

The census of open clusters in the Milky Way is in a never-before seen state of flux. Recent works have reported hundreds of new open clusters thanks to the incredible astrometric quality of the Gaia satellite, but other works have also reported that many open clusters discovered in the pre Gaia era may be associations. We aim to conduct a comparison of clustering algorithms used to detect open clusters, attempting to statistically quantify their strengths and weaknesses by deriving the sensitivity, specificity, and precision of each as well as their true positive rate against a larger sample. We selected DBSCAN, HDBSCAN, and Gaussian mixture models for further study, owing to their speed and appropriateness for use with Gaia data. We developed a preprocessing pipeline for Gaia data and developed the algorithms further for the specific application to open clusters. We derived detection rates for all 1385 open clusters in the fields in our study as well as more detailed performance statistics for 100 of these open clusters. DBSCAN was sensitive to 50% to 62% of the true positive open clusters in our sample, with generally very good specificity and precision. HDBSCAN traded precision for a higher sensitivity of up to 82%, especially across different distances and scales of open clusters. Gaussian mixture models were slow and only sensitive to 33% of open clusters in our sample, which tended to be larger objects. Additionally, we report on 41 new open cluster candidates detected by HDBSCAN, three of which are closer than 500 pc. When used with additional post-processing to mitigate its false positives, we have found that HDBSCAN is the most sensitive and effective algorithm for recovering open clusters in Gaia data. Our results suggest that many more new and already reported open clusters have yet to be detected in Gaia data.

研究动机与目标

• 评估并比较DBSCAN、HDBSCAN和GMMs在Gaia DR2数据中检测疏散星团的性能。
• 量化每种算法在不同星团属性下的灵敏度、特异性、精确率和真正例率。
• 识别现有星团检测方法的局限性，并提高银河系中疏散星团普查的完整性。
• 利用优化的聚类技术检测新的疏散星团候选体，特别是邻近和低表面亮度的天体。
• 为未来基于HDBSCAN的全天空无偏搜索提供基础，通过后处理减轻误报。

提出的方法

• 在开发定制化预处理流程后，将DBSCAN、HDBSCAN和GMMs应用于Gaia DR2的天体测量数据（位置、自行、视差）。
• 采用混合方法处理DBSCAN，通过ACG方法确定epsilon值，以平衡灵敏度与精确率。
• 通过调整其分层聚类结构，增强HDBSCAN对Gaia数据中可变密度和尺度星团的处理能力。
• 优化GMMs用于已知星团的成员星分配，但计算成本限制了其可扩展性。
• 使用1385个已知疏散星团的样本评估算法性能，并对100个代表性星团进行详细分析。
• 对HDBSCAN输出应用后处理以过滤误报，并验证新星团候选体。

实验结果

研究问题

• RQ1 在Gaia DR2数据中检测疏散星团时，DBSCAN、HDBSCAN和GMMs在灵敏度、特异性和精确率方面如何比较？
• RQ2 每种算法在恢复不同距离、大小和密度星团方面存在哪些局限性？
• RQ3 HDBSCAN能否检测到DBSCAN和GMMs遗漏的疏散星团，特别是在高消光或遥远区域？
• RQ4 通过HDBSCAN结合后处理，可可靠识别出多少个新的疏散星团候选体？
• RQ5 先前报告的MWSC星表中的疏散星团在多大程度上实际上是星协或在Gaia数据中不可检测？

主要发现

• HDBSCAN实现了最高的灵敏度，可检测高达82%的真实正向疏散星团，尤其在不同距离和尺度下表现优异。
• DBSCAN检测到50–62%的已知星团，具有高特异性和精确率，但因采用单一全局epsilon参数，在可变密度结构中表现不佳。
• GMMs速度最慢且灵敏度最低，仅检测到33%的星团，主要针对较大的星团，不适合大规模无偏搜索。
• 本研究识别出41个新的疏散星团候选体，其中三个位于500 pc以内，包括一个仅290 pc远的星团，表明本地星团普查存在显著不完整性。
• 多个文献中来自MWSC星表的星团被发现更可能是星协或在Gaia数据中不可检测，提示需重新评估现有星表。
• 经后处理的HDBSCAN结果在未来的全天空无偏搜索中展现出巨大潜力，能更有效地检测暗淡和遥远的星团。