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arXiv 1712.01409astro-ph.SR
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机器学习算法在银河系沃尔夫-拉叶星红外色选择中的应用

Applications of Machine-Learning Algorithms for Infrared Colour Selection of Galactic Wolf-Rayet Stars

Giuseppe Morello, Patrick W. Morris, Schuyler D. Van Dyk, Anthony P. Marston, Jon C. Mauerhan

摘要:

我们已经研究并应用机器学习算法对银河系沃尔夫-拉叶(WR)候选者进行红外颜色选择。银河平面内红外天体的GLIMPSE目录中的天体可以根据其宽带光度星等推断出的颜色分为不同的恒星星族(来自2 MASS的$J$、$H$和$K_s$,以及四个\textit{Spitzer} /IRAC波段)。在这个试点研究中测试的算法是$k$ -最近邻($k$ -NN)的方法,这是理想的探索性研究的变量和类之间的相互关系是复杂的分类问题的变体。这项研究的目的是(1)提供一个自动化的工具来选择可靠的WR候选者和潜在的其他类别的对象,(2)测量执行这些任务时红外颜色选择的效率,(3)为统计推断银河系中WR恒星的总数奠定基础。我们报告了在一组已知物体和选定的候选者上获得的性能结果,我们已经进行了后续的光谱观测,并确认发现了4颗新的WR星。

英文摘要:

We have investigated and applied machine-learning algorithms for Infrared Colour Selection of Galactic Wolf-Rayet (WR) candidates. Objects taken from the GLIMPSE catalogue of the infrared objects in the Galactic plane can be classified into different stellar populations based on the colours inferred from their broadband photometric magnitudes ($J$, $H$ and $K_s$ from 2MASS, and the four \textit{Spitzer}/IRAC bands). The algorithms tested in this pilot study are variants of the $k$-Nearest Neighbours ($k$-NN) approach, which is ideal for exploratory studies of classification problems where interrelations between variables and classes are complicated. The aims of this study are (1) to provide an automated tool to select reliable WR candidates and potentially other classes of objects, (2) to measure the efficiency of infrared colour selection at performing these tasks and, (3) to lay the groundwork for statistically inferring the total number of WR stars in our Galaxy. We report the performance results obtained over a set of known objects and selected candidates for which we have carried out follow-up spectroscopic observations, and confirm the discovery of 4 new WR stars.

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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