학술논문

Effect of Systematic Uncertainties on Density and Temperature Estimates in Coronae of Capella
Document Type
article
Source
The Astrophysical Journal, Vol 968, Iss 2, p 73 (2024)
Subject
X-ray astronomy
Stellar coronal lines
Stellar coronae
Atomic data
Atomic data benchmarking
Astrostatistics
Astrophysics
QB460-466
Language
English
ISSN
1538-4357
Abstract
We estimate the coronal density of Capella using the O vii and Fe xvii line systems in the soft X-ray regime that have been observed over the course of the Chandra mission. Our analysis combines measures of error due to uncertainty in the underlying atomic data with statistical errors in the Chandra data to derive meaningful overall uncertainties on the plasma density of the coronae of Capella. We consider two Bayesian frameworks. First, the so-called pragmatic Bayesian approach considers the atomic data and their uncertainties as fully specified and uncorrectable. The fully Bayesian approach, on the other hand, allows the observed spectral data to update the atomic data and their uncertainties, thereby reducing the overall errors on the inferred parameters. To incorporate atomic data uncertainties, we obtain a set of atomic data replicates, the distribution of which captures their uncertainty. A principal component analysis of these replicates allows us to represent the atomic uncertainty with a lower-dimensional multivariate Gaussian distribution. A t -distribution approximation of the uncertainties of a subset of plasma parameters including a priori temperature information, obtained from the temperature-sensitive-only Fe xvii spectral line analysis, is carried forward into the density- and temperature-sensitive O vii spectral line analysis. Markov Chain Monte Carlo based model fitting is implemented including Multi-step Monte Carlo Gibbs Sampler and Hamiltonian Monte Carlo. Our analysis recovers an isothermally approximated coronal plasma temperature of ≈5 MK and a coronal plasma density of ≈10 ^10 cm ^−3 , with uncertainties of 0.1 and 0.2 dex, respectively.