A unified numerical model of collisional depolarization and broadening rates due to hydrogen atom collisions

TL;DR

This paper develops a numerical model linking collisional broadening and depolarization rates due to hydrogen atom collisions, enabling efficient estimation of collisional data crucial for interpreting solar polarization spectra.

Contribution

It introduces a genetic programming-based method to derive analytical relationships between collisional broadening and depolarization rates, improving data estimation accuracy.

Findings

The model accurately predicts collisional depolarization rates from broadening data.

Genetic programming effectively fits the relationship between different collisional rates.

The approach enhances the efficiency of data estimation for solar polarization analysis.

Abstract

Interpretation of solar polarization spectra accounting for partial or complete frequency redistribution requires data on various collisional processes. Data for depolarization and polarization transfer are needed but often missing, while data for collisional broadening are usually more readily available. Recent work by Sahal-Br\'echot and Bommier concluded that despite underlying similarities in the physics of collisional broadening and depolarization processes, relationships between them are not possible to derive purely analytically. We aim to derive accurate numerical relationships between the collisional broadening rates and the collisional depolarization and polarization transfer rates due to hydrogen atom collisions. Such relationships would enable accurate and efficient estimation of collisional data for solar applications. Using earlier results for broadening and depolarization processes based on general (i.e. not specific to a given atom), semi-classical calculations employing interaction potentials from perturbation theory, genetic programming (GP) has been used to fit the available data and generate analytical functions describing the relationships between them. The predicted relationships from the GP-based model are compared with the original data to estimate the accuracy of the method.