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

Astronomy and Astrophysics – Astrophysics – Solar and Stellar Astrophysics

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

9 pages, 5 figures, accepted for publication in Astronomy and Astrophysics

Scientific paper

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.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Fitting of dust spectra with genetic algorithms - I. Perspectives & Limitations does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Fitting of dust spectra with genetic algorithms - I. Perspectives & Limitations, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Fitting of dust spectra with genetic algorithms - I. Perspectives & Limitations will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-713381

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.