tonalli: an asexual genetic code to characterise APOGEE-2 stellar spectra. I. Validation with synthetic and solar spectra

TL;DR

tonalli is a Python tool that uses an asexual genetic algorithm to efficiently derive stellar parameters from APOGEE-2 spectra, validated with synthetic and solar spectra, and provides uncertainty estimates.

Contribution

It introduces an innovative asexual genetic algorithm approach for stellar parameter estimation from spectroscopic data, validated with synthetic and real spectra.

Findings

High accuracy in parameter estimation demonstrated with synthetic spectra.

Effective uncertainty quantification via Monte Carlo simulations.

Public availability of the spectral library and analysis code.

Abstract

We present tonalli, a spectroscopic analysis python code that efficiently predicts effective temperature, stellar surface gravity, metallicity, $\alpha$-element abundance, and rotational and radial velocities for stars with effective temperatures between 3200 and 6250 K, observed with the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2). tonalli implements an asexual genetic algorithm to optimise the finding of the best comparison between a target spectrum and the continuum-normalised synthetic spectra library from the Model Atmospheres with a Radiative and Convective Scheme (MARCS), which is interpolated in each generation. Using simulated observed spectra and the APOGEE-2 solar spectrum of Vesta, we study the performance, limitations, accuracy and precision of our tool. Finally, a Monte Carlo realisation was implemented to estimate the uncertainties of each derived stellar parameter. The ad hoc continuum-normalised library is publicly available on Zenodo (DOI 10.5281/zenodo.12736546).