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Stellar parameters and abundances for 3.2 million DESI DR1 spectra using machine learning
Nagaraj Vernekar, Michael Hayden, Sara Lucatello
We present a machine-learning pipeline for determining stellar parameters and chemical abundances from spectra observed by the Dark Energy Spectroscopic Instrument (DESI). Building…
MINCE IV. A detailed analysis of 37 metal-poor and subsolar metallicity stars
L. Sgatti, G. Cescutti, L. Lombardo +13
The project called Measuring at Intermediate metallicity Neutron-Capture Elements (MINCE) primarily focuses on the photospheric abundances of several neutron-capture (NC) elements…
Intrinsic iron abundance spreads in globular clusters
J. Schiappacasse-Ulloa, S. Lucatello, L. Magrini +2
Globular Clusters (GCs) host multiple stellar populations, likely composed of a subset of FG stars that enriched the intracluster medium and gave rise to SG stars, each characteris…
LRPayne: Stellar parameters and abundances from low-resolution spectra
Nagaraj Vernekar, Lorenzo Spina, Sara Lucatello +4
Aims. This paper introduces LRPayne, a novel algorithm designed for the efficient determination of stellar parameters and chemical abundances from low-resolution optical spectra, w…
Stellar Population Astrophysics (SPA) with the TNG: NLTE atmospheric parameters and abundances of giant stars in 33 Open Clusters
M. Dal Ponte, V. D'Orazi, A. Bragaglia +11
Open clusters serve as important tools for accurately studying the chemical evolution of the Milky Way. By combining precise chemical data from high-resolution spectra with informa…
Signatures of planets and Galactic subpopulations in solar analogs. Precise chemical abundances with neural networks
Giulia Martos, Jorge Meléndez, Lorenzo Spina +1
The aim of this work is to obtain precise atmospheric parameters and chemical abundances automatically for solar twins and analogs to find signatures of exoplanets, as well as to a…