Spectral decomposition by genetic forward modelling
Judge, P. G.; Brown, J. C.; Charbonneau, P.; McIntosh, S. W.; Diver, D. A.; Ireland, J.
United Kingdom, United States
Abstract
We discuss the analysis of real and simulated line spectra using a genetic forward modelling technique. We show that this Genetic Algorithm (GA) based technique experiences none of the user bias or systematic problems that arise when faced with poorly sampled or noisy data. An important feature of this technique is the ease with which rigid a priori constraints can be applied to the data. These constraints make the GA decomposition much more accurate and stable, especially at the limit of instrumental resolution, than decomposition algorithms commonly in use.