A Bayesian approach to scaling relations for amplitudes of solar-like oscillations in Kepler stars

TL;DR

This study uses Bayesian inference on Kepler data to evaluate and compare different amplitude scaling relations for solar-like oscillations, finding the model with a separate mass dependence to be most favored.

Contribution

It introduces a Bayesian framework to assess and compare amplitude scaling relations across a wide range of stellar types using well-calibrated temperatures.

Findings

The model with separate mass dependence is most favored.

Differences among models vary with stellar evolution phase.

Bayesian analysis effectively discriminates between competing models.

Abstract

We investigate different amplitude scaling relations adopted for the asteroseismology of stars that show solar-like oscillations. Amplitudes are among the most challenging asteroseismic quantities to handle because of the large uncertainties that arise in measuring the background level in the star's power spectrum. We present results computed by means of a Bayesian inference on a sample of 1640 stars observed with Kepler, spanning from main sequence to red giant stars, for 12 models used for amplitude predictions and exploiting recently well-calibrated effective temperatures from SDSS photometry. We test the candidate amplitude scaling relations by means of a Bayesian model comparison. We find the model having a separate dependence upon the mass of the stars to be largely the most favored one. The differences among models and the differences seen in their free parameters from early to late phases of stellar evolution are also highlighted.