Searching for Possible Exoplanet Transits from BRITE Data through a Machine Learning Technique
This work provides a more efficient method for astronomers to identify potential exoplanet transits from large photometric datasets, potentially accelerating the discovery of new exoplanets.
This paper developed a machine learning technique using convolutional neural networks to search for exoplanet transits in BRITE satellite photometric light curves. The method achieved over 99.7% accuracy in identifying transit candidates, leading to a small set of ten candidates, two of which (HD37465 and HD186882) were prioritized for further observation.
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.