Detecting solar-like oscillations

We develop a fast statistical test of the null hypothesis that a Fourier-like power spectrum is consistent with noise. The null hypothesis is rejected where the local 'coefficient of variation' (CV) - the ratio of the standard deviation to the mean - in a power spectrum deviates significantly from expectations for pure noise (CV~1.0 for a {Chi}^2^-degrees-of-freedom distribution).

This technique is of particular utility for detecting signals in power spectra with frequency-dependent noise backgrounds, as it is only sensitive to features that are sharp relative to the inspected frequency bin width. We develop a CV-based algorithm to quickly detect the presence of solar-like oscillations in photometric power spectra that are dominated by stellar granulation. This approach circumvents the need for background fitting to measure the frequency of maximum solar-like oscillation power, {nu}_max_.