Advanced fit technique for astrophysical spectra

Aims.The purpose of this paper is to introduce a robust method of data fitting convenient for dealing with astrophysical spectra contaminated by a large fraction of outliers. Methods.We base our approach on the suitable defined measure: the density of the least squares (DLS) that characterizes subsets of the whole data set. The best-fit parameters are obtained by the least-square method on a subset having the maximum value of DLS or, less formally, on the largest subset free of outliers. Results.We give the FORTRAN90 source code of the subroutine that implements the DLS method. The efficiency of the DLS method is demonstrated on a few examples: estimation of continuum in the presence of spectral lines, estimation of spectral line parameters in the presence of outliers, and estimation of the thermodynamic temperature from the spectrum that is rich in spectral lines. Conclusions.Comparison of the present results with the ones obtained with the widely used comprehensive multi-component fit yields agreement within error margins. Due to simplicity and robustness, the proposed approach could be the method of choice whenever outliers are present, or whenever unwelcome features of the spectrum are to be considered as formal outliers (e.g. spectral lines while estimating continuum)