Searching for Possible Exoplanet Transits from BRITE Data through a Machine Learning Technique. (4th December 2020)

• Record Type: Journal Article
• Authors: Yeh, Li-Chin; Jiang, Ing-Guey
• Is Part Of: Publications of the Astronomical Society of the Pacific. Volume 133:Number 1019(2021)
• Publication Date: 2020-12-04
• Subjects: Time series analysis -- Exoplanet detection methods -- Exoplanet systems; Astronomy -- Periodicals; Astronomy; Periodicals; Periodicals; 520.5
• DOI: 10.1088/1538-3873/abbb24
• Languages: English
• ISSNs: 0004-6280

Abstract

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/~jiang/BRITE2020YehJiangCNN.tar.gz .