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

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