Magnetic activity in stars manifests as dark spots on their surfaces that modulate the brightness observed by telescopes. These light curves contain important information on stellar rotation. However, the accurate estimation of rotation periods is computationally expensive due to scarce ground truth information, noisy data, and large parameter spaces that lead to degenerate solutions. We harness the power of deep learning and successfully apply Convolutional Neural Networks to regress stellar rotation periods from Kepler light curves. Geometry-preserving time-series to image transformations of the light curves serve as inputs to a ResNet-18 based architecture which is trained through transfer learning. The McQuillan catalog of published rotation periods is used as ansatz to groundtruth. We benchmark the performance of our method against a random forest regressor, a 1D CNN, and the Auto-Correlation Function (ACF) - the current standard to estimate rotation periods. Despite limiting our input to fewer data points (1k), our model yields more accurate results and runs 350 times faster than ACF runs on the same number of data points and 10,000 times faster than ACF runs on 65k data points. With only minimal feature engineering our approach has impressive accuracy, motivating the application of deep learning to regress stellar parameters on an even larger scale

翻译：恒星的磁性活动表现为其表面的暗点,它能调节望远镜观测到的亮度。这些光曲线包含恒星旋转的重要信息。 但是,精确估计旋转周期在计算上非常昂贵, 原因是缺少地面真相信息、数据噪音和导致溶液退化的大型参数空间。 我们利用深层学习的力量,成功地运用进化神经网络从开普勒光曲线中回溯星轮周期。 几何测量保存光曲线图像转换的时间序列,作为ResNet-18基础结构的投入,该结构是通过传输学习培训的。 公布的旋转周期的McQuillan编目被用作地铁解密。 我们用随机森林递增者、 1D CNN 和 Auto-Corrulation 函数(ACF) 来衡量我们的方法的性能, 尽管我们的投入限于较少的数据点(1k),但我们模型产生的准确结果比ACF-18的建筑结构结构要快350倍。 公布的旋转周期的McQuillan编集, 用作地铁图。 我们的精确度比ACF的精确度要快到65级的深度数据速度要快得多。