Searching for Possible Exoplanet Transits from BRITE Data through a Machine Learning Technique

Abstract

The photometric light curves of BRITE satellites were examined through a machine learning technique to investigate whether there are possible exoplanets moving around nearby bright stars. Focusing on different transit periods, several convolutional neural networks were constructed to search for transit candidates. The convolutional neural networks were trained with synthetic transit signals combined with BRITE light curves until the accuracy rate was higher than 99.7 $\%$. Our method could efficiently lead to a small number of possible transit candidates. Among these ten candidates, two of them, HD37465, and HD186882 systems, were followed up through future observations with a higher priority. The codes of convolutional neural networks employed in this study are publicly available at http://www.phys.nthu.edu.tw/$\sim$jiang/BRITE2020YehJiangCNN.tar.gz.

23 pages, 4 tables, 8 figures, published by PASP

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• The GPU Phase Folding and Deep Learning Method for Detecting Exoplanet Transits
• A Possible Converter to Denoise the Images of Exoplanet Candidates through Machine Learning Techniques