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arXiv 2108.01640astro-ph.SRastro-ph.IMcs.LG
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使用深度学习方法自动分类食双星

Automatic classification of eclipsing binary stars using deep learning methods

Pavol Jozef Šafárik University in Košice(科希策帕沃尔·约瑟夫·沙法里克大学);Technical University of Košice(科希策工业大学)

Michal Čokina, Viera Maslej-Krešňáková, Peter Butka, Štefan Parimucha

摘要:

在过去的几十年里,在开发机器人望远镜方面取得了巨大的进展,因此,天空调查(地面和空间)已成为大量新观测数据的来源。这些数据包含了大量关于双星的信息,隐藏在它们的光变曲线中。由于收集了大量的天文数据,期望所有数据都进行手动处理和分析是不合理的。因此,在本文中,我们将重点放在使用深度学习方法对食双星进行自动分类。我们的分类器提供了一个工具,分类的光变曲线的双星分为两类:分离和过度接触。我们使用ELISA软件获得合成数据,然后将其用于分类器的训练。为了评估的目的,我们收集了100个观测到的双星的光变曲线,以评估一些分类器。我们将半分离食双星评估为分离。性能最好的分类器结合了双向长短期记忆(LSTM)和一维卷积神经网络,在评估集上实现了98%的准确率。忽略半分离食双星,我们可以获得100%的分类准确率。

英文摘要:

In the last couple of decades, tremendous progress has been achieved in developing robotic telescopes and, as a result, sky surveys (both terrestrial and space) have become the source of a substantial amount of new observational data. These data contain a lot of information about binary stars, hidden in their light curves. With the huge amount of astronomical data gathered, it is not reasonable to expect all the data to be manually processed and analyzed. Therefore, in this paper, we focus on the automatic classification of eclipsing binary stars using deep learning methods. Our classifier provides a tool for the categorization of light curves of binary stars into two classes: detached and over-contact. We used the ELISa software to obtain synthetic data, which we then used for the training of the classifier. For evaluation purposes, we collected 100 light curves of observed binary stars, in order to evaluate a number of classifiers. We evaluated semi-detached eclipsing binary stars as detached. The best-performing classifier combines bidirectional Long Short-Term Memory (LSTM) and a one-dimensional convolutional neural network, which achieved 98% accuracy on the evaluation set. Omitting semi-detached eclipsing binary stars, we could obtain 100% accuracy in classification.

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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