A new method to detect solar-like oscillations at very low S/N using statistical significance testing

TL;DR

This paper presents MWPS, a new statistical method for detecting solar-like oscillations in stellar data with very low S/N, outperforming existing techniques by identifying excess power signatures reliably.

Contribution

The paper introduces MWPS, a novel statistical approach that improves detection of solar-like oscillations at low S/N compared to traditional methods.

Findings

MWPS outperforms PSxPS in low S/N conditions

MWPS reliably detects solar-like oscillations in degraded solar data

The false-alarm approach effectively distinguishes true signals from noise

Abstract

We introduce a new method to detect solar-like oscillations in frequency power spectra of stellar observations, under conditions of very low signal to noise. The Moving-Windowed-Power-Search, or MWPS, searches the power spectrum for signatures of excess power, over and above slowly varying (in frequency) background contributions from stellar granulation and shot or instrumental noise. We adopt a false-alarm approach (Chaplin et al. 2011) to ascertain whether flagged excess power, which is consistent with the excess expected from solar-like oscillations, is hard to explain by chance alone (and hence a candidate detection). We apply the method to solar photometry data, whose quality was systematically degraded to test the performance of the MWPS at low signal-to-noise ratios. We also compare the performance of the MWPS against the frequently applied power-spectrum-of-power-spectrum (PSxPS) detection method. The MWPS is found to outperform the PSxPS method.