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.

通过卷积神经网络，该论文提供了一种高效、高性能的方法，能够在大规模存档搜索中进行预测和识别短周期变量，从而揭示了短周期主序双星和 Delta Scuti 星等两个主要种群的特征。