2019PASP..131j8001H

We develop a novel method based on machine-learning principles to achieve optimal initiation of CPU-intensive computations for forward asteroseismic modeling in a multi-dimensional parameter space. A deep neural network is trained on a precomputed asteroseismology grid containing about 62 million coherent oscillation-mode frequencies derived from stellar evolution models. These models are representative of the core-hydrogen-burning stage of intermediate-mass and high-mass stars. The evolution models constitute a 6D parameter space and their predicted low-degree pressure- and gravity-mode oscillations are scanned using a genetic algorithm. A software pipeline is created to find the best-fitting stellar parameters for a given set of observed oscillation frequencies. The proposed method finds the optimal regions in the 6D parameter space in less than a minute, hence providing the optimal starting point for further and more detailed forward asteroseismic modeling in a high-dimensional context. We test and apply the method to seven pulsating stars that were previously modeled asteroseismically by classical grid-based forward modeling based on a χ² statistic, and obtain good agreement with past results. Our deep-learning methodology opens up the application of asteroseismic modeling in +6D parameter space for thousands of stars pulsating in coherent modes with long lifetimes observed by the Kepler space telescope and to be discovered with the TESS and PLATO space missions, while applications so far have been done star-by-star for only a handful of cases. Our method is open source and can be freely used by anyone.³