Determination of the mass loss rate and the terminal velocity of stellar winds. I Genetic algorithm for automatic line profile fitting

TL;DR

This paper introduces a genetic algorithm-based method to automatically determine stellar wind parameters, such as mass loss rate and terminal velocity, from spectral line profiles, especially useful when high-resolution data is unavailable.

Contribution

It presents a novel genetic algorithm approach for objective, automatic fitting of complex stellar wind line profiles to estimate key parameters.

Findings

Successfully tested on synthetic data with known parameters

First application to real stellar spectral data shows promising results

Provides a computationally efficient alternative to high-resolution spectroscopy

Abstract

Terminal wind velocity and mass loss rate are the most fundamental parameters of stellar winds. Unfortunately, their determination calls for high resolution spectroscopy in a range of wavelengths spanning from the ultraviolet to the infrared. For weak and/or distant objects, this becomes unfeasible. Nevertheless, it is possible to obtain an accurate estimate of these parameters through a simplified study of the formation processes of resonant lines which show P Cyg profiles. In this case, the line profile is a complex function of 6 parameters, with the radiative transport treatment becoming relatively inexpensive. However, preforming a 6 dimensional parameter fit raises a number of problems if one seeks an objective and automatic procedure to yield the optimal values, from which wind velocities and mass loss rates can be estimated. Useing a Likelihood function to construct a well defined statistical estimator of the goodness of fit which corresponds to a given model, we turn to a genetic algorithm through which we find the global maximum of the 6 dimensional Likelihood hyper-surface. We here present the implementation of the method, its successful testing with synthetic line profiles, where the answer is known in advance, together with first results of its application to real data