The GALAH Survey: Stellar parameters and abundances for 800,000 Gaia RVS spectra using GALAH DR4 and The Cannon

TL;DR

This paper uses a data-driven approach to derive stellar parameters and abundances from nearly one million Gaia RVS spectra by transferring information from a higher-resolution GALAH catalogue, enabling detailed chemical analysis of Milky Way stars.

Contribution

We developed a method using The Cannon to accurately predict stellar parameters and abundances from Gaia RVS spectra by training on GALAH data, extending detailed chemical analysis to a vast number of stars.

Findings

Validated metallicity estimates with high precision (~0.02-0.10 dex)

Recovered bimodal Ti/Fe distribution reflecting Milky Way disc populations

Demonstrated potential for detailed chemical analysis of Gaia RVS dataset

Abstract

Analysing stellar parameters and abundances from nearly one million Gaia Data Release 3 (DR3) Radial Velocity Spectrometer (RVS) spectra poses challenges due to the limited spectral coverage (restricted to the infrared Ca II triplet) and variable signal-to-noise ratios of the data. To address this, we use The Cannon, a data-driven method, to transfer stellar parameters and abundances from the GALAH (GALactic Archaeology with HERMES) DR4 (R ~ 28,000) catalogue to the lower resolution Gaia DR3 RVS spectra (R ~ 11,500). Our model, trained on 14,484 common targets, predicts parameters such as $T_{\text{eff}}$, $\log g$, and $[\text{Fe/H}]$, along with several other elements across approximately 800,000 Gaia RVS spectra. We utilise stars from open and globular clusters present in the Gaia RVS catalogue to validate our predicted mean $[\text{Fe/H}]$ with high precision (~0.02-0.10 dex). Additionally, we recover the bimodal distribution of $[\text{Ti/Fe}]$ versus $[\text{Fe/H}]$, reflecting the high and low $\alpha$-components of Milky Way disc stars, demonstrating The Cannon's capability for accurate stellar abundance determination from medium-resolution Gaia RVS spectra. The methodologies and resultant catalogue presented in this work highlight the remarkable potential of the RVS dataset, which by the end of the Gaia mission will comprise spectra of over 200 million stars.