Probing the Milky Way Halo with RR Lyrae Stars from Gaia Data Release 3

Abstract

The Milky Way (MW) stellar halo, containing debris from past accretion events, serves as a fossil record of hierarchical mass assembly. Due to their distinct properties, RR Lyrae stars (RRLs) serve as excellent tracers for identifying and characterising the halo's substructures. We analysed a sample of 4933 RRLs, for which we calculated the integrals of motion and orbital parameters. We applied the domain-informed novelty detection CLustering in Multiphase Boundaries (CLiMB) framework to identify RRL membership in the MW substructures. We analysed the metallicity distributions of RRLs in major accreted system remnants as a snapshot of their chemical evolutionary status during early epochs. We calculated the weighted mean metallicity ([Fe/H]) and the corresponding standard deviation for Gaia Sausage/Enceladus ([Fe/H] = $-1.57 \pm 0.25$ dex), Sequoia ([Fe/H] =$ -1.64\pm0.26$ dex), and the Helmi streams ([Fe/H] = $-1.66\pm0.19$ dex). The metallicity distribution of RRLs in Thamnos was found to be bimodal, with the metal-poor peak likely representing the genuine accreted Thamnos population ([Fe/H] = $-1.94\pm0.20$ dex), in agreement with recent works based on spectroscopic abundances. Our analysis shows that the substructures ED-1 and L-RL3 are highly contaminated by thick disc stars. However, the metal-poor tails in their metallicity distributions may be signatures of remnants from small accreted systems. We also identify over-densities of RRLs in correspondence with the recently reported substructures Shiva and Shakti, which we suggest are of in-situ origin. Finally, we applied the RRL-based mass-metallicity relation of galaxies to test the nature of the identified dynamical substructures.

Figures (11)

• Figure 1: Distribution of 4933 RRLs from our sample in the $E$-$L_z$ and $L_z$-$L_\perp$ planes, colour-coded by the substructure to which they belong. Grey dots represent RRLs not assigned to any substructure. The top panels show RRLs identified in known substructures by cross-matching with D23, while the bottom panels display RRLs assigned to known substructures during the first phase of the CLiMB algorithm.
• Figure 2: Distribution of 4933 RRLs from our sample in the $E$–$L_z$ and $L_z$–$L_\perp$ planes, colour-coded by the substructure to which they were assigned during the second phase of the CLiMB algorithm. Grey dots represent RRLs not assigned to any substructure
• Figure 3: Distribution of 3614 RRLs from the reference sample, for which uncertainties in photometric metallicities are less than $0.5~\text{dex}$, on the Cartesian $Y$- $X$ ($\textit{left}$) and $Z$-$X$ ($\textit{right}$) planes, colour-coded by metallicity.
• Figure 4: Distribution of RRLs in the disk-like structure identified with the CLiMB algorithm in the $E$ versus $L_z$ ( upper left), Cartesian $Z$ versus $X$ ( upper right), and $Z_{\mathrm{max}}$ versus eccentricity ( bottom left) planes, colour-coded by metallicity. The black dashed line outlines the region used to select the thin-disk RRLs. Bottom right: Metallicity distributions of RRLs in the thin and thick disks, selected based on their $Z_{\mathrm{max}}$ and eccentricity. See text for details.
• Figure 5: Metallicity distribution of RRLs (light blue bins) in the known substructures of the MW halo. The dashed red line indicates the mean metallicity of RRLs in each substructure. Name of the substructure, number of RRLs with accurate metallicities, and mean metallicities are indicated in the legend. The uncertainties are calculated as the weighted standard deviation of the mean. For substructures containing only one star, the uncertainty corresponds to the photometric metallicity uncertainty of that individual star.