论文标题
ROTNET：使用卷积神经网络对恒星旋转周期的快速和可扩展估计
RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks
论文作者
论文摘要
恒星中的磁活动表现为其表面上的黑斑，可调节望远镜观察到的亮度。这些光曲线包含有关恒星旋转的重要信息。但是，由于稀缺的地面真相信息，嘈杂的数据和大型参数空间，旋转周期的准确估计在计算上是昂贵的，这些空间会导致溶液退化。我们利用深度学习的力量，并成功地将卷积神经网络从开普勒光曲线中恢复出色的旋转周期。几何形状保留的时间序列图像光曲线的图像转换是基于RESNET-18的架构的输入，该架构是通过转移学习训练的。已发表旋转期的麦奎兰目录用作地面确实的Ansatz。我们根据随机森林回归剂，1D CNN和自动相关函数（ACF）对方法的性能进行基准测试 - 当前估计旋转周期的标准。尽管我们的输入限制为较少的数据点（1K），但我们的模型的结果比ACF在相同数量的数据点上运行的速度更高，并且比ACF在65K数据点上运行的速度快350倍。只有最小的功能工程，我们的方法具有令人印象深刻的精度，激发了深度学习以更大的规模回归恒星参数的应用
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
