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

TL;DR

This study applies convolutional neural networks to BRITE satellite light curves to identify potential exoplanet transits, successfully narrowing down candidates for further observation with high accuracy.

Contribution

It introduces a machine learning approach using CNNs trained on synthetic signals to efficiently detect exoplanet transit candidates in BRITE data.

Findings

High accuracy (>99.7%) in transit detection

Identification of 10 potential transit candidates

Follow-up prioritized for two promising systems

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.