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arXiv 2101.04019astro-ph.EPastro-ph.GAastro-ph.IMastro-ph.SR
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高节奏微透镜光变曲线的分类I:定义特征

Classifying High-cadence Microlensing Light Curves I; Defining Features

Somayeh Khakpash, Joshua Pepper, Matthew Penny, B. Scott Gaudi, R. A. Street

摘要:

微透镜是发现寒冷系外行星的有力工具,罗马太空望远镜的微透镜调查将发现1000多颗这样的行星。快速,自动分类罗马的微透镜事件可以用来优先考虑最有趣的事件的后续观察。机器学习现在经常用于天文学中的分类问题,但这种算法的成功可以依赖于适当特征的定义,这些特征捕获可以映射到感兴趣参数的观测的基本元素。在本文中,我们介绍了我们已经开发的工具,以捕捉不同类型的微透镜事件的模拟罗马光变曲线的功能,并评估其有效性分类微透镜光变曲线。这些特征被量化为可用于决定给定光变曲线是由于特定类型的微透镜事件的可能性的参数。这种方法给我们留下了一系列参数,这些参数描述了峰的平滑度、对称性、峰的数量以及与主峰的小偏差的宽度和高度等特征。这将使我们能够快速分析一组微透镜光变曲线,并随后使用所得参数作为机器学习算法的输入来对事件进行分类。

英文摘要:

Microlensing is a powerful tool for discovering cold exoplanets, and the The Roman Space Telescope microlensing survey will discover over 1000 such planets. Rapid, automated classification of Roman's microlensing events can be used to prioritize follow-up observations of the most interesting events. Machine learning is now often used for classification problems in astronomy, but the success of such algorithms can rely on the definition of appropriate features that capture essential elements of the observations that can map to parameters of interest. In this paper, we introduce tools that we have developed to capture features in simulated Roman light curves of different types of microlensing events, and evaluate their effectiveness in classifying microlensing light curves. These features are quantified as parameters that can be used to decide the likelihood that a given light curve is due to a specific type of microlensing event. This method leaves us with a list of parameters that describe features like the smoothness of the peak, symmetry, the number of peaks, and width and height of small deviations from the main peak. This will allow us to quickly analyze a set of microlensing light curves and later use the resulting parameters as input to machine learning algorithms to classify the events.

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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