An upgraded interpolator of the radial basis functions network for spectral calculation based on empirical stellar spectral library

TL;DR

This paper introduces an improved radial basis function interpolator for empirical stellar spectral libraries, enhancing spectral interpolation accuracy in stellar population synthesis models, especially compared to previous methods.

Contribution

The authors develop a novel RBF interpolator that incorporates star density and anisotropic features, significantly improving spectral interpolation accuracy over existing methods.

Findings

The new interpolator performs well across most of the parameter space.

It shows clear improvement over previous interpolation methods.

The method is efficient and easy to use for spectral synthesis.

Abstract

Stellar population synthesis is an important method in the galaxy and star-cluster studies. In the stellar population synthesis models, stellar spectral library is necessary for the integrated spectra of the stellar population. Usually, the stellar spectral library is used to the transformation between the stellar atmospheric parameters and the stellar spectrum. The empirical stellar spectral library has irreplaceable advantages than the theoretical library. However, for the empirical spectral library, the distribution of stars is irregularly in the stellar atmospheric parameter space, this makes the traditional interpolator difficult to get the accurate results. In this work, we will provide an improved radial basis function interpolator which is used to obtain the interpolated stellar spectra based on the empirical stellar spectral library. For this interpolator, we use the relation between the standard variance {\sigma} in the Gaussian radial basis function and the density distribution of stars in the stellar atmospheric parameter space to give the prior constraint on this {\sigma}. Moreover, we also consider the anisotropic radius basis function by the advantage of the local dispersion of stars in the stellar atmospheric parameter space. Furthermore, we use the empirical stellar spectral library MILES to test this interpolator. On the whole, the interpolator has a good performance except for the edge of the low-temperature region. At last, we compare this interpolator with the work in Cheng et al. (2018), the interpolation result shows an obvious improvement. Users can use this interpolator to get the interpolated spectra based on the stellar spectral library quickly and easily.