Abstract

In hot plasmas, such as the ones encountered in astrophysics or laser-fusion studies, the number of ionic excited states may become huge, and the relevant electron configurations cannot always be handled individually. The Super Transition Array approach enables one to calculate the massic photo-absorption cross-section (or radiative opacity) in a statistical manner consisting of grouping configurations close in energy into superconfigurations. One of the main issues of the method, beyond its spectral resolution, is the determination of the most relevant configurations that contribute to opacity. In this work, we discuss different aspects of the generation of superconfigurations in a hot plasma and propose a new adaptive algorithm.

Highlights

  • The radiative opacity is an essential quantity governing the structure and evolution of stars

  • One may turn to the unresolved transition arrays (UTA) method [1], which assumes that all lines in the spectrum of each configurationto-configuration excitation merge into a single effective line, which can be depicted by a Gaussian function

  • Is a superconfiguration for which the first three supershells coincide with the normal lowest shells while each one of the others contains three shells supposed to be close in energy

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Summary

Introduction

The radiative opacity is an essential quantity governing the structure and evolution of stars. The numerical cost resulting from the huge number of transitions becomes prohibitive In this case, one may turn to the unresolved transition arrays (UTA) method [1], which assumes that all lines in the spectrum of each configurationto-configuration excitation merge into a single effective (super-) line, which can be depicted by a Gaussian function. The efficiency of the UTA method comes from the fact that compact formulas are available for the three lowest energy moments (orders 0, 1 and 2) of the line-strength weighted line energies of a transition array. Such moments are expressed in terms of reduced matrix elements of the dipole operator, Slater integrals, and subshell occupation numbers.

Configuration Probabilities Based on the Average-Atom Model
Uncorrelated Probability Law
Correlated Probability Law
Correlated Gaussian Approximations
Supershells, Superconfigurations
Linearization of the Energy of a Superconfiguration
Number of Configurations, Weight of a Superconfiguration
Populating a Partition in Superconfigurations Ξ from the Average-Atom Results
Calculation of NB
Estimating Statistical Weights
Algorithm “Divide and Conquer”
Algorithm “Divide and Conquer”: General Procedure
Energy Criterion
Link to the Master Theorem
Examples and Results
Generating Superconfigurations Using the Correlated Gaussian Approximation for Configurations
Illustration ofmethod the method described in Section
Effective (Ionization) Temperature
Binary Supershell Split Algorithm
Conclusions
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