文章基本信息

标题：Identifying Planetary Transit Candidates in TESS Full-frame Image Light Curves via Convolutional Neural Networks
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作者：Greg Olmschenk ; Stela Ishitani Silva ; Gioia Rau 等
期刊名称：The Astronomical journal
印刷版ISSN：0004-6256
电子版ISSN：1538-3881
出版年度：2021
卷号：161
期号：6
页码：1-15
DOI：10.3847/1538-3881/abf4c6
语种：English
出版社：American Institute of Physics
摘要：The Transiting Exoplanet Survey Satellite (TESS) mission measured light from stars in ∼75% of the sky throughout its 2 yr primary mission, resulting in millions of TESS 30-minute-cadence light curves to analyze in the search for transiting exoplanets. To search this vast data trove for transit signals, we aim to provide an approach that both is computationally efficient and produces highly performant predictions. This approach minimizes the required human search effort. We present a convolutional neural network, which we train to identify planetary transit signals and dismiss false positives. To make a prediction for a given light curve, our network requires no prior transit parameters identified using other methods. Our network performs inference on a TESS 30-minute-cadence light curve in ∼5 ms on a single GPU, enabling large-scale archival searches. We present 181 new planet candidates identified by our network, which pass subsequent human vetting designed to rule out false positives. Our neural network model is additionally provided as open-source code for public use and extension.