RotNet：利用卷积神经网络快速可扩展地估算恒星自转周期
RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks
摘要：
恒星中的磁场活动表现为它们表面的黑点，这些黑点调节了望远镜观测到的亮度。这些光变曲线包含了恒星自转的重要信息。然而，旋转周期的准确估计是计算昂贵的，由于稀缺的地面真值信息，噪声数据，和大的参数空间，导致退化的解决方案。我们利用深度学习的力量，成功地应用卷积神经网络从开普勒光变曲线回归恒星自转周期。光变曲线的几何保持时间序列到图像的转换作为基于ResNet-18的架构的输入，该架构通过迁移学习进行训练。McQuillan公布的自转周期表被用作地面实况的补充。我们将我们的方法的性能与随机森林回归器，1D CNN和自动关联函数（ACF）-当前估计旋转周期的标准进行基准测试。尽管将我们的输入限制为更少的数据点（1 k），但我们的模型产生了更准确的结果，并且比ACF在相同数量的数据点上运行的速度快350倍，比ACF在65 k数据点上运行的速度快10，000倍。我们的方法只需要最小的特征工程就具有令人印象深刻的准确性，从而激励深度学习应用于更大规模的恒星参数回归
英文摘要：
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
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