The GPU phase folding and deep learning method for detecting exoplanet transits

• Published in: Monthly notices of the Royal Astronomical Society Vol. 528; no. 3; pp. 4053 - 4067
• Main Authors: Wang, Kaitlyn, Ge, Jian, Willis, Kevin, Wang, Kevin, Zhao, Yinan
• Format: Journal Article
• Language: English
• Published: London Oxford University Press 01.03.2024
• Subjects: Algorithms; Artificial neural networks; Extrasolar planets; Folding; Graphics processing units; Light curve; Machine learning; Orbits; Planet detection; Signal to noise ratio; Transit; surveys; methods: data analysis; catalogues; planets and satellites: detection; techniques: photometric

ABSTRACT 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...