Vetting Asteroseismic $\Delta\nu$ Measurements using Neural Networks

Precise asteroseismic parameters allow one to quickly estimate radius and mass distributions for large samples of stars. A number of automated methods are available to calculate the frequency of maximum acoustic power ($\nu_{\mathrm{max}}$) 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. Our classifier is independent of the method used to obtain $\nu_{\mathrm{max}}$ and $\Delta\nu$, and therefore can be applied as a final step to any such method. Tests of our classifier's performance on manually vetted $\Delta\nu$ measurements reach an accuracy of 95%. We apply the method to giants observed by K2 Galactic Archaeology Program and find that our results retain stars with astrophysical oscillation parameters consistent with the parameter distributions already defined by well-characterised Kepler red giants.