In the last decade, exoplanets space missions started to collect a huge amount of photometric observations, with over ∼1,000,000 new light curves generated every month from the Transiting Exoplanet Survey Satellite (TESS) full-frame images alone. In order to analyze such an unprecedented volume of data, automated planet-candidate detection has become an appreciable replacement to human vetting. In this work, we present a Machine Learning approach, based on Deep Neural Networks, to perform a binary classification of TESS light curves in terms of planet candidate and not-planet. Since few TESS labeled data exist to date, we pre-train the network with Kepler DR24 data set, including ≳15,000 labeled light curves. Our pre-trained model is then tested on ExoFOP data, showing an appreciable gain in terms of reliability with respect to a randomly initialized model.

在过去十年中，系外行星太空任务开始收集大量的光度观测数据，仅通过凌日系外行星测量卫星 (TESS) 全帧图像，每月就会产生超过 1,000,000 条新的光变曲线。为了分析如此大量的数据，自动行星候选检测已成为人工审查的可观替代品。在这项工作中，我们提出了一种基于深度神经网络的机器学习方法，用于根据行星候选和非行星对 TESS 光曲线进行二元分类。由于迄今为止很少有 TESS 标记数据，我们使用 Kepler DR24 数据集对网络进行了预训练，包括 ≳15,000 条标记光曲线。然后在 ExoFOP 数据上测试我们的预训练模型，