SteParSyn: A Bayesian code to infer stellar atmospheric parameters using spectral synthesis
H. M. Tabernero, E. Marfil, D. Montes, J. I. Gonz\'alez Hern\'andez

TL;DR
SteParSyn is a Python-based Bayesian spectral synthesis code that accurately infers stellar atmospheric parameters for FGKM stars, validated against well-studied targets and available to the community.
Contribution
It introduces a novel spectral synthesis method using MCMC and spectral emulators for precise stellar parameter estimation.
Findings
Achieves internal scatter of -12 ± 117 K in Teff
Provides log(g) consistent within 0.1 dex with trigonometric measurements
Accurately estimates parameters within 50 K, 0.1 dex, and 0.05 dex for stars with vsini ≤ 30 km/s
Abstract
Context: SteParSyn is an automatic code written in Python 3.X designed to infer the stellar atmospheric parameters Teff, log(g), and [Fe/H] of FGKM-type stars following the spectral synthesis method. Aims: We present a description of the SteParSyn code and test its performance against a sample of late-type stars that were observed with the HERMES spectrograph mounted at the 1.2-m Mercator Telescope. This sample contains 35 late-type targets with well-known stellar parameters determined independently from spectroscopy. The code is available to the astronomical community in a GitHub repository. Methods: SteParSyn uses a Markov chain Monte Carlo (MCMC) sampler to explore the parameter space by comparing synthetic model spectra generated on the fly to the observations. The synthetic spectra are generated with an spectral emulator. Results: We computed Teff, log(g), and [Fe/H] for our…
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Taxonomy
TopicsStellar, planetary, and galactic studies · Astronomy and Astrophysical Research · Astronomical Observations and Instrumentation
