论文标题
通过机器学习技术搜索从Brite数据的可能的系外行星过渡
Searching for Possible Exoplanet Transits from BRITE Data through a Machine Learning Technique
论文作者
论文摘要
通过机器学习技术检查了Brite卫星的光度光曲线，以研究是否有可能在附近的明亮恒星周围移动。关注不同的过境期，建立了几个卷积神经网络以寻找过境候选者。卷积神经网络经过合成传输信号和Brite光曲线的训练，直到准确率高于99.7 $ \％$。我们的方法可以有效地导致少量可能的过境候选者。在这十个候选人中，其中两个，HD37465和HD186882系统，通过以后的观察结果进行了跟踪，优先级更高。这项研究中采用的卷积神经网络的守则可在http://www.phys.nthu.edu.tw/quly \ simjiang/brite2020yehjiangcnn.tar.tar.gz上公开获得。
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.
