A comparison of pendulum models for large-amplitude longitudinal prominence oscillations

TL;DR

This study compares original and extended pendulum models for large-amplitude prominence oscillations, using Bayesian inference to evaluate their impact on magnetic field strength estimates and model plausibility.

Contribution

It quantifies differences between models in magnetic field inference and demonstrates the extended model's higher plausibility through Bayesian analysis.

Findings

Bayesian posteriors for magnetic field strength are well-constrained.

Differences between models increase with oscillation period.

Extended model is more plausible across observed periods.

Abstract

Large-amplitude prominence oscillations offer diagnostic information relevant to understanding the magnetic and plasma structure of solar prominences. Accurate prominence seismology requires the use of reliable models. The so-called pendulum model for large-amplitude longitudinal prominence oscillations has demonstrated robustness against observations and numerical simulations. Recent improvements have extended the model to situations with non-uniform gravity, thus leading to corrections that have implications for the inference of the magnetic field strength. In this study we quantify how the different model predictions given by the original and extended pendulum models impact the inference of the minimum magnetic field strength derived from the observed periods of large-amplitude longitudinal prominence oscillations. The analysis we conducted follows a Bayesian approach to solve the inference problem and assess the absolute and relative plausibilities of the two considered models in explaining the observed data, with their uncertainty. We find that the Bayesian solution to the inference problem provides well-constrained posteriors for the minimum magnetic field strength. However, the solutions from each adopted model differ, with differences increasing with the oscillation period. A model comparison analysis results in the extended model being more plausible in the full range of observed periods. However, the magnitude of the Bayes factor is not large enough to determine whether there is positive evidence supporting any of the models. We suggest computing model-averaged posteriors as the most reasonable solution to the inference problem.