Chemical analysis of the Milky Way's Nuclear Star Cluster: Evidence for a metallicity gradient

The Milky Way nuclear star cluster (MWNSC) is located together with its surrounding nuclear stellar disc (MWNSD) in the Galactic centre and they dominate the gravitational potential within the inner 300,pc. However, the formation and evolution of both systems and their possible connections are still under debate. We reanalyse the low-resolution KMOS spectra in the MWNSC with the aim to improve the stellar parameters ($\rm T_{eff}$, $\rm \log,g$, and $\rm [M/H])$ for the MWNSC. We use an improved line-list, especially dedicated for cool M giants allowing to improve the stellar parameters and to obtain in addition global $\rm α$-elements. A comparison with high-resolution IR spectra (IGRINS) gives very satisfactory results pinning down the uncertainties to $\rm T_{eff} \simeq 150,K$, $\rm log,g \simeq 0.4,dex$, and $\rm [M/H] \simeq 0.2,dex$. Our $\rm α$-elements agree within 0.1,dex compared to the IGRINS spectra. We obtain a high-quality sample of 1140 M giant stars where we see an important contribution of a metal-poor population ($\rm \sim 20,%$) centered at $\rm [M/H] \simeq -0.7,dex$ while the most dominant part comes from the metal-rich population with $\rm [M/H] \simeq 0.26,dex$. We construct a metallicity map and find a metallicity gradient of $\rm \sim -0.1 \pm 0.02 ,dex/pc$ favouring the inside-out formation scenario for the MWNSC.

💡 Research Summary

This paper presents a comprehensive re‑analysis of the low‑resolution KMOS near‑infrared spectra of stars in the Milky Way’s nuclear star cluster (MWNSC), employing an updated line list and NLTE corrections specifically optimized for cool M giants. The authors generated a new synthetic spectral grid using the SME code coupled with MARCS model atmospheres, incorporating recent atomic data and molecular transitions (CN, CO, SiO, H₂O, OH) as well as pre‑computed NLTE departure coefficients for Mg, Si, Ca, and Fe. By feeding this grid into the Bayesian fitting framework STARKIT and using the MultiNest nested‑sampling algorithm, they derived effective temperatures, surface gravities, overall metallicities (