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.

GPFC 是一种新颖的图形处理单元（GPU）相位折叠和卷积神经网络（CNN）系统，用于利用凌日法检测系外行星。该系统采用在 GPU 上并行化的快速折叠算法来放大信噪比低的凌日信号，从而以高精度和速度进行搜索。经过对两百万个合成光曲线进行训练的 CNN 报告了每个周期可能性的得分，GPFC 在速度上比现有的盒形拟合最小二乘（BLS）方法快了三个数量级。模拟结果显示，与 BLS 相比，GPFC 实现了 97% 的训练准确性，在相同的误报率下具有更高的真阳性率和在相同的检测率下具有更高的准确性。在 Kepler 光曲线中进行盲搜索时，GPFC 可以完全恢复已知的超短周期行星。这些结果突显了 GPFC 作为替代传统 BLS 算法在 Kepler 和其他空间凌日任务（如 K2、TESS 和未来的 PLATO 和 Earth 2.0）的数据中寻找新的凌日行星的潜力。