arXiv 2205.092802022-05-19astro-ph.GA

使用机器学习从M87的地基光度测量中识别河外球状星团候选者

Using Machine Learning to Identify Extragalactic Globular Cluster Candidates from Ground-Based Photometric Surveys of M87

Emilia Barbisan, Jeff Huang, Kristen C. Dage, Daryl Haggard, Robin Arnason, Arash Bahramian, William I. Clarkson, Arunav Kundu, Stephen E. Zepf

备注：: 14 pages, 9 figures, accepted to MNRAS
DOI：: 10.1093/mnras/stac1396

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摘要：

球状星团（GCs）一直是天文学许多子领域中许多长期存在的问题的核心，因此，系统地识别外部星系中的GCs具有巨大的影响。在这项研究中，我们利用M87的研究良好的GC系统来实现监督机器学习（ML）分类算法-特别是随机森林和神经网络-使用加拿大-法国-夏威夷望远镜（CFHT）的地基测光从前景恒星和背景星系中识别GC。我们将这两种ML分类方法与“人类选择”GC的研究进行比较，发现性能最好的随机森林模型可以重新选择61.2% $\pm$ 8.0%的从HST数据（ACSVCS）中选择的GC，性能最好的神经网络模型可以重新选择95.0% $\pm$ 3.4%。当与人类分类的GC和从CFHT数据中选择的污染物相比时-独立于我们的训练数据-表现最好的随机森林模型可以正确地分类91.0% $\pm$ 1.2%，表现最好的神经网络模型可以正确地分类57.3% $\pm$ 1.1%。天文学中的ML方法一直受到极大的关注，Vera C.鲁宾天文台准备迎接第一缕曙光。本研究中的观测值被选择为与早期的鲁宾天文台数据直接可比，并且在即将到来的数据集上运行ML算法的前景产生了有希望的结果。

英文摘要：

Globular clusters (GCs) have been at the heart of many longstanding questions in many sub-fields of astronomy and, as such, systematic identification of GCs in external galaxies has immense impacts. In this study, we take advantage of M87's well-studied GC system to implement supervised machine learning (ML) classification algorithms - specifically random forest and neural networks - to identify GCs from foreground stars and background galaxies using ground-based photometry from the Canada-France-Hawai'i Telescope (CFHT). We compare these two ML classification methods to studies of "human-selected" GCs and find that the best performing random forest model can reselect 61.2% $\pm$ 8.0% of GCs selected from HST data (ACSVCS) and the best performing neural network model reselects 95.0% $\pm$ 3.4%. When compared to human-classified GCs and contaminants selected from CFHT data - independent of our training data - the best performing random forest model can correctly classify 91.0% $\pm$ 1.2% and the best performing neural network model can correctly classify 57.3% $\pm$ 1.1%. ML methods in astronomy have been receiving much interest as Vera C. Rubin Observatory prepares for first light. The observables in this study are selected to be directly comparable to early Rubin Observatory data and the prospects for running ML algorithms on the upcoming dataset yields promising results.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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