اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدBayesian Inversion of Stokes Profiles
133
0
0.0
(
0
)
تأليف
A. Asensio Ramos IAC
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
[abridged] Inversion techniques are the most powerful methods to obtain information about the thermodynamical and magnetic properties of solar and stellar atmospheres. In the last years, we have witnessed the development of highly sophisticated inversion codes that are now widely applied to spectro-polarimetric observations. The majority of these inversion codes are based on the optimization of a complicated non-linear merit function. However, no reliable and statistically well-defined confidence intervals can be obtained for the parameters inferred from the
قيم البحث
اقرأ أيضاً
Quantitative thermodynamical, dynamical and magnetic properties of the solar and stellar plasmas are obtained by interpreting their emergent non-polarized and polarized spectrum. This inference requires the selection of a set of spectral lines partic
ularly sensitive to the physical conditions in the plasma and a suitable parametric model of the solar/stellar atmosphere. Nonlinear inversion codes are then used to fit the model to the observations. However, the presence of systematic effects like nearby or blended spectral lines, telluric absorption or incorrect correction of the continuum, among others, can strongly affect the results. We present an extension to current inversion codes that can deal with these effects in a transparent way. The resulting algorithm is very simple and can be applied to any existing inversion code with the addition of a few lines of code as an extra step in each iteration.
Spectropolarimetric
The level set approach has proven widely successful in the study of inverse problems for interfaces, since its systematic development in the 1990s. Recently it has been employed in the context of Bayesian inversion, allowing for the quantification of
uncertainty within the reconstruction of interfaces. However the Bayesian approach is very sensitive to the length and amplitude scales in the prior probabilistic model. This paper demonstrates how the scale-sensitivity can be circumvented by means of a hierarchical approach, using a single scalar parameter. Together with careful consideration of the development of algorithms which encode probability measure equivalences as the hierarchical parameter is varied, this leads to well-defined Gibbs based MCMC methods found by alternating Metropolis-Hastings updates of the level set function and the hierarchical parameter. These methods demonstrably outperform non-hierarchical Bayesian level set methods.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
