This paper presents GPFC, a novel Graphics Processing Unit (GPU) Phase Folding and Convolutional Neural Network (CNN) system to detect exoplanets using the transit method. We devise a fast folding algorithm parallelized on a GPU to amplify low signal-to-noise ratio transit signals, allowing a search at high precision and speed. A CNN trained on two million synthetic light curves reports a score indicating the likelihood of a planetary signal at each period. GPFC improves on speed by three orders of magnitude over the predominant Box-fitting Least Squares (BLS) method. Our simulation results show GPFC achieves 97% training accuracy, higher true positive rate at the same false positive rate of detection, and higher precision at the same recall rate when compared to BLS. GPFC recovers 100% of known ultra-short-period planets in Kepler light curves from a blind search. These results highlight the promise of GPFC as an alternative approach to the traditional BLS algorithm for finding new transiting exoplanets in data taken with Kepler and other space transit missions such as K2, TESS and future PLATO and Earth 2.0.

翻译：本文提出GPFC系统——一种新型的基于图形处理器（GPU）的相位折叠与卷积神经网络（CNN）系统，用于通过凌星法探测系外行星。我们设计了一种在GPU上并行执行的快速折叠算法，可放大低信噪比的凌星信号，实现高精度与高速搜索。基于两百万条合成光变曲线训练的CNN为每个周期输出表征行星信号概率的评分。GPFC在速度上较主流方法——箱型最小二乘法（BLS）提升三个数量级。仿真结果表明，与BLS相比，GPFC的训练准确率达97%，在相同误报率下具有更高的真阳性率，并在相同召回率下具有更高的精度。通过盲搜索，GPFC可100%恢复开普勒光变曲线中已知的超短周期行星。这些结果突显了GPFC作为传统BLS算法替代方案的潜力，可用于处理开普勒、K2、TESS等空间凌星任务以及未来PLATO和Earth 2.0任务的数据，以搜寻新的凌星系外行星。