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arXiv 2108.00670astro-ph.EPastro-ph.IMcs.LG
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使用基于深度学习的目标检测算法识别光变曲线信号。I. Transit检测

Identify Light-Curve Signals with Deep Learning Based Object Detection Algorithm. I. Transit Detection

Tsung-Dao Lee Institute, Shanghai Jiao Tong University(上海交通大学李政道研究所);CAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences(中国科学院国家天文台光学天文重点实验室);School of Astronomy and Space Sciences, University of Chinese Academy of Sciences(中国科学院大学天文与空间科学学院);WHU-NAOC Joint Center for Astronomy, Wuhan University(武汉大学武汉大学-国家天文台联合天文中心)

Kaiming Cui, Junjie Liu, Fabo Feng, Jifeng Liu

摘要:

深度学习技术在凌日系外行星领域已经得到了很好的探索;然而,以前的工作主要集中在分类和检查上。在这项工作中,我们开发了一种新的检测算法的基础上,在计算机视觉领域的一个行之有效的目标检测框架。通过在已确认的开普勒系外行星的光变曲线上训练网络,我们的模型在识别信噪比高于6的凌日时产生了约90%的准确率和召回率(将置信度阈值设置为0.6)。如果置信阈值稍低,召回率可以达到95%以上。我们还将训练好的模型转移到TESS数据上,并获得了类似的性能。我们的算法的结果符合人类的视觉感知的直觉,并使它有助于找到单过境的候选人。此外,输出边界框的参数也可以帮助找到多行星系统。我们的网络和检测功能在Deep-Transit工具包中实现,这是一个托管在GitHub和PyPI上的开源Python包。

英文摘要:

Deep learning techniques have been well explored in the transiting exoplanet field; however, previous work mainly focuses on classification and inspection. In this work, we develop a novel detection algorithm based on a well proven object detection framework in the computer vision field. Through training the network on the light curves of the confirmed Kepler exoplanets, our model yields about 90% precision and recall for identifying transits with signal-to-noise ratio higher than 6 (set the confidence threshold to 0.6). Giving a slightly lower confidence threshold, recall can reach higher than 95%. We also transfer the trained model to the TESS data and obtain similar performance. The results of our algorithm match the intuition of the human visual perception and make it useful to find single-transiting candidates. Moreover, the parameters of the output bounding boxes can also help to find multiplanet systems. Our network and detection functions are implemented in the Deep-Transit toolkit, which is an open-source Python package hosted on GitHub and PyPI.

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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