webSME: An online tool to infer stellar parameters and abundances

Abstract

Stellar spectroscopy is a robust technique for determining fundamental stellar parameters such as effective temperature, surface gravity and metallicity. Spectroscopy Made Easy (SME) has long served as a framework for spectral synthesis and parameter inference. In this paper, we introduce webSME, a web-based extension of the Python implementation of SME. webSME integrates enhancements including a non-local thermodynamic equilibrium abundance correction mode, a precomputed grid of synthetic spectra for robust determination of stellar parameters from large wavelength ranges (thousands of Angstroms wide), Markov Chain Monte Carlo sampling for uncertainty estimation, and support for recent reference abundance patterns. It enables efficient and user-friendly analysis of high-resolution spectra, making it suitable for a wide range of applications - from detailed abundance studies to education and outreach. We demonstrate the performance of webSME on synthetic and observed spectra, including benchmark stars, and validate its accuracy against classical SME-based analysis. The platform's ease of access and advanced capabilities position it as a powerful tool in the modern astrophysical toolkit.