Fundamental Parameters of Main-Sequence Stars in an Instant with Machine Learning

TL;DR

This paper introduces a machine learning approach for quickly estimating fundamental parameters of main-sequence stars from observational data, achieving comparable accuracy to traditional methods with significantly less computation.

Contribution

The authors develop an open-source machine learning method that rapidly determines stellar parameters from classical and asteroseismic data, improving efficiency over existing techniques.

Findings

Estimates are comparable to traditional methods in accuracy.

The method enables exploration of many stellar parameters efficiently.

Evidence for an empirical diffusion-mass relation is presented.

Abstract

Owing to the remarkable photometric precision of space observatories like Kepler, stellar and planetary systems beyond our own are now being characterized en masse for the first time. These characterizations are pivotal for endeavors such as searching for Earth-like planets and solar twins, understanding the mechanisms that govern stellar evolution, and tracing the dynamics of our Galaxy. The volume of data that is becoming available, however, brings with it the need to process this information accurately and rapidly. While existing methods can constrain fundamental stellar parameters such as ages, masses, and radii from these observations, they require substantial computational efforts to do so. We develop a method based on machine learning for rapidly estimating fundamental parameters of main-sequence solar-like stars from classical and asteroseismic observations. We first demonstrate this method on a hare-and-hound exercise and then apply it to the Sun, 16 Cyg A & B, and 34 planet-hosting candidates that have been observed by the Kepler spacecraft. We find that our estimates and their associated uncertainties are comparable to the results of other methods, but with the additional benefit of being able to explore many more stellar parameters while using much less computation time. We furthermore use this method to present evidence for an empirical diffusion-mass relation. Our method is open source and freely available for the community to use. The source code for all analyses and for all figures appearing in this manuscript can be found electronically at https://github.com/earlbellinger/asteroseismology