NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data

• Published in: The Astronomical journal Vol. 167; no. 6; pp. 265 - 280
• Main Authors: Asa’d, Randa, Hernandez, S., John, J. M, Alfaro-Cuello, M., Wang, Z., As’ad, A., Vasini, A., Matteucci, F.
• Format: Journal Article
• Language: English
• Published: Madison The American Astronomical Society 01.06.2024; IOP Publishing
• Subjects: Abundance; Chemical abundances; Chromium; Galactic clusters; Galactic evolution; Galaxies; Iron; Large Magellanic Cloud; Machine learning; Magellanic clouds; Magnesium; Nickel; Silicon; Spectroscopy; Spectrum analysis; Star clusters; Stars; Titanium
• ISSN: 0004-6256; 1538-3881
• DOI: 10.3847/1538-3881/ad3f1b

Abstract We present the first application of the novel approach based on data-driven machine learning methods applied to Multi-Unit Spectroscopic Explorer (MUSE) field data to derive stellar abundances of star clusters. MUSE has been used to target more than 10,000 fields, and it is unique in its ability to study dense stellar fields such as stellar clusters providing spectra for each individual star. We use MUSE data of the extragalactic young stellar cluster NGC 1856, located in the Large Magellanic Cloud (LMC). We present the individual stellar [Fe/H] abundance of 327 cluster members in addition to [Mg/Fe], [Si/Fe], [Ti/Fe], [C/Fe], [Ni/Fe], and [Cr/Fe] abundances of subsample sets. Our results match the LMC abundances obtained in the literature for [Mg/Fe], [Ti/Fe], [Ni/Fe], and [Cr/Fe]. This study is the first to derive [Si/Fe] and [C/Fe] abundances for this cluster. The revolutionary combination of integral-field spectroscopy and data-driven modeling will allow us to understand the chemical enrichment of star clusters and their host galaxies in greater detail expanding our understanding of galaxy evolution.