Fitting of dust spectra with genetic algorithms - I. Perspectives & Limitations

TL;DR

This paper introduces an automated spectral fitting method for AGB star IR spectra using genetic algorithms, demonstrating improved fits and potential for better dust composition modeling, though further testing is needed.

Contribution

It combines the DUSTY radiative transfer code with the PIKAIA genetic algorithm to automate and enhance spectral fitting of AGB star dust spectra.

Findings

Routine successfully improved existing spectral fits.

Slightly altered dust compositions can yield better fits.

Method shows promise but requires further validation.

Abstract

Aims: We present an automatised fitting procedure for the IR range of AGB star spectra. Furthermore we explore the possibilities and boundaries of this method. Methods: We combine the radiative transfer code DUSTY with the genetic algorithm PIKAIA in order to improve an existing spectral fit significantly. Results: In order to test the routine we carried out a performance test by feeding an artificially generated input spectrum into the program. Indeed the routine performed as expected, so, as a more realistic test set-up, we tried to create model fits for ISO spectra of selected AGB stars. Here we were not only able to improve existing fits, but also to show that a slightly altered dust composition may give a better fit for some objects. Conclusion: The use of a genetic algorithm in order to automatise the process of fitting stellar spectra seems to be very promising. We were able to improve existing fits and further offer a quantitative method to compare different models with each other. Nevertheless this method still needs to be studied and tested in more detail.