The Transiting Exoplanet Survey Satellite (TESS) mission measured light from stars in ~75% of the sky throughout its two year primary mission, resulting in millions of TESS 30-minute cadence light curves to analyze in the search for transiting exoplanets. To search this vast data trove for transit signals, we aim to provide an approach that is both computationally efficient and produces highly performant predictions. This approach minimizes the required human search effort. We present a convolutional neural network, which we train to identify planetary transit signals and dismiss false positives. To make a prediction for a given light curve, our network requires no prior transit parameters identified using other methods. Our network performs inference on a TESS 30-minute cadence light curve in ~5ms on a single GPU, enabling large scale archival searches. We present 181 new planet candidates identified by our network, which pass subsequent human vetting designed to rule out false positives. Our neural network model is additionally provided as open-source code for public use and extension.
翻译:过境Explanenet调查卫星(TESS)飞行任务测量了天空中~75%的恒星的光线,在其为期两年的主要任务中测量了星体在~75%的天空中的光线,结果产生了数百万个30分钟的长距光线曲线,用于在寻找过境外行星时进行分析。为了搜索这个巨大的数据巡洋图以探测过境信号,我们的目标是提供一种既具有计算效率又能产生高性能预测的方法。这个方法最大限度地减少了所需的人类搜索努力。我们展示了一个动态神经网络,我们通过培训来识别行星过境信号并排除虚假的阳性。为了预测特定的光曲线,我们的网络不需要使用其他方法事先确定的任何中转参数。我们的网络在单一的GPUPS上用~5米的30分钟长宽度光曲线进行推断,以便能够进行大规模的档案搜索。我们介绍了我们的网络所查明的181个新的行星候选人,这些候选人随后经过人类审查,目的是排除假阳性。我们的神经网络模型作为公开源码供公众使用和扩展。