arXiv2025
Aims. This paper introduces LRPayne, a novel algorithm designed for the efficient determination of stellar parameters and chemical abundances from low-resolution optical spectra, with a primary focus on data from large-scale galactic surveys such as WEAVE. Methods. LRPayne employs a model-driven approach, utilising a fully connected artificial neural network (ANN), trained on a library of 70,000 synthetic stellar spectra generated using iSpec with 1D MARCS model atmospheres and the Turbospectrum synthesis code. The network is trained to predict normalized flux given stellar labels (Teff, log(g), [Fe/H], vmic, vmax and v sin i, and 24 individual elemental abundances). Stellar parameters are subsequently derived from observed spectra by finding the best-fit synthetic spectrum from the ANN using a chi-squared minimisation technique. The method operates on spectra degraded to a resolution of R=5000 covering the wavelength range 4200-6900 {\AA}. Results. Internal accuracy tests on synthetic spectra show a median interpolation error of less than 0.13 % for 90 % of the validation sample. The method accurately recovers most input labels from synthetic spectra, even at a signal-to-noise ratio (S/N) of 20, with some expected challenges for elements like Li, K, and N. Validation on observed spectra of 25 Gaia FGK benchmark stars and 42 metal-poor stars reveals good agreement with literature values. For stellar parameters, mean differences are 22+-87 K for Teff , 0.19+-0.23 dex for log(g), and 0.01+-0.17 dex for [Fe/H]. Abundances for elements like Na, Mg, Si, and most Fe-peak elements (Cr, Ni, V, Sc) are well-recovered. Challenges are noted for oxygen, manganese in metal-rich giants, aluminium in metal-poor stars and dwarfs, and for deriving log g in hot metal-poor dwarfs, partly due to non-local thermodynamic equilibrium effects and line characteristics.