Stellar parameters and abundances for 3.2 million DESI DR1 spectra using machine learning

Abstract

We present a machine-learning pipeline for determining stellar parameters and chemical abundances from spectra observed by the Dark Energy Spectroscopic Instrument (DESI). Building on the model-driven Payne framework, we train an artificial neural network on ~370,000 synthetic spectra spanning FGK stars, and fit the network directly to the flux-calibrated, un-normalised spectral energy distribution (SED) of each star, rather than to a continuum-normalised spectrum as done by DESI's official Stellar Parameter (SP) pipeline. Internal accuracy tests show a median interpolation error of less than 0.3% for 90% of a synthetic verification sample, and the full fitting workflow recovers input labels to high accuracy even at low signal-to-noise ratio (S/N). Validating our method on cross-matched samples of 6719 APOGEE and 3455 GALAH stars observed by DESI with S/N > 20, we recover Teff and logg with smaller systematic offsets than the DESI SP pipeline, an improvement we attribute to fitting the full SED shape rather than a normalised spectrum. We further recover 12 elemental abundances (Na, Mg, Al, Si, Ca, Ti, V, Cr, Mn, Ni, Ba, and Y) to an accuracy that matches or exceeds SP where a comparison is possible, and provide four abundances, Mn, V, Ba, and Y, that SP does not report at all. Applying this pipeline to the DESI Data Release 1 stellar sample, we deliver a catalogue of stellar parameters and abundances for 3.2 million stars.