Issue |
A&A
Volume 677, September 2023
|
|
---|---|---|
Article Number | A23 | |
Number of page(s) | 5 | |
Section | The Sun and the Heliosphere | |
DOI | https://doi.org/10.1051/0004-6361/202346834 | |
Published online | 25 August 2023 |
Bayesian evidence for two slow-wave damping models in hot coronal loops
1
Instituto de Astrofísica de Canarias, 38205 La Laguna, Tenerife, Spain
e-mail: iarregui@iac.es
2
Departamento de Astrofísica, Universidad de La Laguna, 38206 La Laguna, Tenerife, Spain
3
Centre for Fusion, Space and Astrophysics, Physics Department, University of Warwick, Coventry CV4 7AL, UK
4
Engineering Research Institute “Ventspils International Radio Astronomy Centre (VIRAC)” of Ventspils University of Applied Sciences, Inzenieru iela 101, Ventspils 3601, Latvia
5
Centro de Investigacion en Astronomía, Universidad Bernardo O’Higgins, Avenida Viel 1497, Santiago, Chile
Received:
8
May
2023
Accepted:
3
July
2023
We computed the evidence in favour of two models, one based on field-aligned thermal conduction alone and another that includes thermal misbalance as well, to explain the damping of slow magneto-acoustic waves in hot coronal loops. Our analysis is based on the computation of the marginal likelihood and the Bayes factor for the two damping models. We quantified their merit to explain the apparent relationship between slow mode periods and damping times, measured with SOHO/SUMER in a set of hot coronal loops. The results indicate evidence in favour of the model with thermal misbalance in the majority of the sample, with a small population of loops for which thermal conduction alone is more plausible. The apparent possibility of two different regimes of slow-wave damping, if due to differences between the loops of host active regions and/or the photospheric dynamics, may help to reveal the coronal heating mechanism.
Key words: magnetohydrodynamics (MHD) / methods: statistical / Sun: corona / Sun: oscillations
© The Authors 2023
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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