Inferring physical parameters of solar filaments from simultaneous longitudinal and transverse oscillations

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Authors: Upasna Baweja, Vaibhav Pant, I. Arregui, M. Faizan KHAN
Cited by: 0

Abstract

Context. Different modes of oscillations are frequently observed in solar prominences, and prominence seismology helps estimate important physical parameters such as the magnetic field strength. Although the simultaneous detection of longitudinal and transverse oscillations in the same filament is not common, such rare observations provide a unique opportunity to constrain the physical parameters of interest. Aims. In this study, we aim to estimate the physical parameters of prominences undergoing simultaneous longitudinal and transverse oscillations. Methods. We applied Bayesian seismology techniques to observations of longitudinal and transverse filament oscillations to infer the magnetic field strength, the length, and the number of twists in the flux tube holding the prominence plasma. We first used the observations of longitudinal oscillations and the pendulum model to infer the posterior probability density for the magnetic field strength. The obtained marginal posterior of the magnetic field, combined with the observations of the transverse oscillations, was then used to estimate the probable values of the length of the magnetic flux tube that supports the filament material using Bayesian inference. This estimated length was used to compute the number of twists in the flux tube. Results. For the prominences under study, we find that the length of the flux tubes supporting the quiescent prominences can be very large (from 100 to 1000 Mm), and the number of twists in the flux tube is not more than three. Conclusions. Our results demonstrate that Bayesian analysis offers valuable methods for parameter inference in prominence seismology.

Fields of Study & Subjects

Solar and Space Plasma Dynamics Stellar planetary and galactic studies Magnetic confinement fusion research

Publication details

Journal / source
Springer Link (Chiba Institute of Technology)
Publication date
28 April 2026
Language
EN