Detecting Solar-like Oscillations in Red Giants with Deep Learning

Time-resolved photometry of tens of thousands of red giant stars from space missions like Kepler and K2 has created the need for automated asteroseismic analysis methods. The first and most fundamental step in such analysis is to identify which stars show oscillations. It is critical that this step be performed with no, or little, detection bias, particularly when performing subsequent ensemble analyses that aim to compare the properties of observed stellar populations with those from galactic models. However, an efficient, automated solution to this initial detection step still has not been found, meaning that expert visual inspection of data from each star is required to obtain the highest level of detections. Hence, to mimic how an expert eye analyzes the data, we use supervised deep learning to not only detect oscillations in red giants, but also to predict the location of the frequency at maximum power, ν _max, by observing features in 2D images of power spectra. By training on Kepler data, we benchmark our deep-learning classifier against K2 data that are given detections by the expert eye, achieving a detection accuracy of 98% on K2 Campaign 6 stars and a detection accuracy of 99% on K2 Campaign 3 stars. We further find that the estimated uncertainty of our deep-learning-based ν _max predictions is about 5%. This is comparable to human-level performance using visual inspection. When examining outliers, we find that the deep-learning results are more likely to provide robust ν _max estimates than the classical model-fitting method.