Vetting asteroseismic {Delta}{nu} measurements

A number of automated methods are available to calculate the frequency of maximum acoustic power ({nu}_max_) and the frequency separation between overtone modes ({Delta}{nu}) from the power spectra of red giants. However, filtering through the results requires either manual vetting, elaborate averaging across multiple methods, or sharp cuts in certain parameters to ensure robust samples of stars free of outliers.

Given the importance of ensemble studies for Galactic archaeology and the surge in data availability, faster methods for obtaining reliable asteroseismic parameters are desirable. We present a neural network classifier that vets {Delta}{nu} by combining multiple features from the visual {Delta}{nu} vetting process. Our classifier is able to analyse large numbers of stars determining whether their measured {Delta}{nu} are reliable thus delivering clean samples of oscillating stars with minimal effort.