Determining stellar atmospheric parameters and chemical abundances of FGK stars with iSpec

TL;DR

This paper introduces iSpec, a Python-based, platform-independent software framework that automates the derivation of stellar atmospheric parameters and chemical abundances from high-resolution spectra, validated on Gaia FGK benchmark stars.

Contribution

The paper presents iSpec, an integrated, open-source spectroscopic tool that combines synthetic spectral fitting and equivalent width methods for stellar analysis, with validation on benchmark stars.

Findings

iSpec accurately derives stellar parameters from high-quality spectra.

The software performs well across different analysis methods.

Validation confirms reliability on Gaia FGK benchmark stars.

Abstract

Context. An increasing number of high-resolution stellar spectra is available today thanks to many past and ongoing extensive spectroscopic surveys. Consequently, the scientific community needs automatic procedures to derive atmospheric parameters and individual element abundances. Aims. Based on the widely known SPECTRUM code by R. O. Gray, we developed an integrated spectroscopic software framework suitable for the determination of atmospheric parameters (i.e., effective temperature, surface gravity, metallicity) and individual chemical abundances. The code, named iSpec and freely distributed, is written mainly in Python and can be used on different platforms. Methods. iSpec can derive atmospheric parameters by using the synthetic spectral fitting technique and the equivalent width method. We validated the performance of both approaches by developing two different pipelines and analyzing the Gaia FGK benchmark stars spectral library. The analysis was complemented with several tests designed to assess other aspects, such as the interpolation of model atmospheres and the performance with lower quality spectra. Results. We provide a code ready to perform automatic stellar spectral analysis. We successfully assessed the results obtained for FGK stars with high-resolution and high signal-to-noise spectra.