RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks

TL;DR

This paper introduces a deep learning approach using Convolutional Neural Networks to efficiently and accurately estimate stellar rotation periods from light curves, significantly outperforming traditional methods in speed and accuracy.

Contribution

The authors develop a CNN-based method that transforms light curves into images for faster, more accurate stellar rotation period estimation, outperforming existing techniques.

Findings

Model achieves higher accuracy than traditional methods.

Runs 350 times faster than ACF on same data points.

Operates efficiently on fewer data points.

Abstract

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