论文标题
使用公民科学标签深入学习长期的系外行星
Discovering Long-period Exoplanets using Deep Learning with Citizen Science Labels
论文作者
论文摘要
鉴于现代望远镜调查的规模，自动化的行星过境检测对于优先考虑候选人的优先级候选者至关重要。尽管由于光曲线的周期性而有效地进行了短期外系外行星检测的当前方法，但缺乏检测单键盘事件的强大方法。但是，《星球猎人苔丝（PHT）》项目最近收集的志愿者标签的过渡现在提供了一个前所未有的机会，可以调查数据驱动的长期外部系外行星检测方法。在这项工作中，我们训练一个1D卷积神经网络，使用PHT志愿者得分作为训练数据对行星转移进行分类。我们发现使用志愿者得分可显着提高综合数据的性能，并以与志愿者相匹配的精确和比率恢复已知行星。重要的是，该模型还恢复了志愿者发现的转移，但由于当前的自动化方法而错过了。
Automated planetary transit detection has become vital to prioritize candidates for expert analysis given the scale of modern telescopic surveys. While current methods for short-period exoplanet detection work effectively due to periodicity in the light curves, there lacks a robust approach for detecting single-transit events. However, volunteer-labelled transits recently collected by the Planet Hunters TESS (PHT) project now provide an unprecedented opportunity to investigate a data-driven approach to long-period exoplanet detection. In this work, we train a 1-D convolutional neural network to classify planetary transits using PHT volunteer scores as training data. We find using volunteer scores significantly improves performance over synthetic data, and enables the recovery of known planets at a precision and rate matching that of the volunteers. Importantly, the model also recovers transits found by volunteers but missed by current automated methods.
