The Transiting Exoplanet Survey Satellite (TESS) mission measured light from stars in ~85% 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 dataset, we aim to provide an approach that is both computationally efficient, produces highly performant predictions, and minimizes the required human search effort. We present a convolutional neural network that we train to identify short period variables. To make a prediction for a given light curve, our network requires no prior target 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 a collection of 14156 short-period variables identified by our network. The majority of our identified variables fall into two prominent populations, one of short-period main sequence binaries and another of Delta Scuti stars. Our neural network model and related code is additionally provided as open-source code for public use and extension.

翻译：凌星系外行星巡天卫星（TESS）任务在为期两年的主任务期间测量了约85%天区中恒星的光度，由此产生了数百万条30分钟采样间隔的光变曲线用于搜寻凌星系外行星。为分析这一海量数据集，我们致力于提供一种兼具计算高效性、高预测性能且能最大限度减少人工搜寻投入的方法。我们提出一种用于识别短周期变星的卷积神经网络。该网络在对给定光变曲线进行预测时，无需预先通过其他方法确定目标参数。网络在单个GPU上可在约5毫秒内完成对一条TESS 30分钟采样间隔光变曲线的推理运算，从而支持大规模档案数据检索。我们展示了由网络识别的14156颗短周期变星集合。其中大多数识别变量属于两个显著族群——短周期主序双星和盾牌座δ型星。我们同时以开源形式提供神经网络模型及相关代码，供公众使用与扩展。