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.

中文翻译：

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通过机器学习技术从 BRITE 数据中搜索可能的系外行星凌日

通过机器学习技术检查了 BRITE 卫星的光度曲线，以研究是否有可能的系外行星围绕附近的明亮恒星移动。针对不同的运输周期，构建了几个卷积神经网络来搜索运输候选者。卷积神经网络使用合成传输信号结合 BRITE 光变曲线进行训练，直到准确率高于 99.7%。我们的方法可以有效地导致少数可能的过境候选者。在这十个候选中，其中两个，HD37465 和 HD186882 系统，通过更高优先级的未来观察进行了跟踪。本研究中使用的卷积神经网络代码可在 http://www.phys.nthu.edu.tw/∼jiang/BRITE2020YehJiangCNN.tar.gz 公开获得。