A probabilistic method for detecting solar-like oscillations using meaningful prior information

TL;DR

This paper presents an automated algorithm to detect solar-like oscillations in stellar photometric data, leveraging prior knowledge and scaling relations to analyze TESS and future PLATO observations effectively.

Contribution

The novel algorithm combines frequency excess detection and pattern recognition to identify solar-like oscillations, improving automation and accuracy over previous methods.

Findings

Achieves 94.7% true positive rate with 8.2% false positives.

Reduces false positive rate to ~2% with 80% true positive rate.

Effective for main-sequence and subgiant stars in TESS data.

Abstract

Current and future space-based observatories such as the Transiting Exoplanet Survey Satellite (TESS) and PLATO are set to provide an enormous amount of new data on oscillating stars, and in particular stars that oscillate similar to the Sun. Solar-like oscillators constitute the majority of known oscillating stars and so automated analysis methods are becoming an ever increasing necessity to make as much use of these data as possible. Here we aim to construct an algorithm that can automatically determine if a given time series of photometric measurements shows evidence of solar-like oscillations. The algorithm is aimed at analyzing data from the TESS mission and the future PLATO mission, and in particular stars in the main-sequence and subgiant evolutionary stages. The algorithm first tests the range of observable frequencies in the power spectrum of a TESS light curve for an excess that is consistent with that expected from solar-like oscillations. In addition, the algorithm tests if a repeating pattern of oscillation frequencies is present in the time series, and whether it is consistent with the large separation seen in solar-like oscillators. Both methods use scaling relations and observations which were established and obtained during the CoRoT, Kepler, and K2 missions. Using a set of test data consisting of visually confirmed solar-like oscillators and nonoscillators observed by TESS, we find that the proposed algorithm can attain a $94.7\%$ true positive rate and a $8.2\%$ false positive rate at peak accuracy. However, by applying stricter selection criteria, the false positive rate can be reduced to $\approx2\%$, while retaining an $80\%$ true positive rate.