The GPU Phase Folding and Deep Learning Method for Detecting Exoplanet
Transits
- URL: http://arxiv.org/abs/2312.02063v2
- Date: Sun, 21 Jan 2024 21:41:32 GMT
- Title: The GPU Phase Folding and Deep Learning Method for Detecting Exoplanet
Transits
- Authors: Kaitlyn Wang, Jian Ge, Kevin Willis, Kevin Wang, Yinan Zhao
- Abstract summary: GPFC is a fast folding algorithm parallelized on a GPU to amplify low signal-to-noise ratio transit signals.
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.
- Score: 4.281682100876565
- License: http://creativecommons.org/licenses/by/4.0/
- 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 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.
While the GPFC method has broad applicability across period ranges, this
research specifically focuses on detecting ultra-short-period planets with
orbital periods less than one day. 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 $\textit{Kepler}$ 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 $\textit{Kepler}$ and other space transit missions such as
K2, TESS and future PLATO and Earth 2.0.
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