Substructure in the stellar halo near the Sun. II. Characterisation of independent structures

Abstract

Context. In an accompanying paper by Lövdal et al. (2022), we presented a data-driven method for clustering in Integrals of Motion space and applied it to a large sample of nearby halo stars with 6D phase-space information. The algorithm identified a large number of clusters, many of which could tentatively be merged into larger groups. Aims. Our goal is to establish the reality of the clusters and groups through a combined study of their stellar populations (average age, metallicity and chemical and dynamical properties) to gain more insights into the accretion history of the Milky Way. Methods. To this end, we develop a procedure that quantifies the similarity of clusters based on the Kolmogorov-Smirnov (KS) test using their metallicity distribution functions, and an isochrone fitting method to determine their average age, which is also used to compare the distribution of stars in the Colour-Absolute magnitude diagram. Taking also into consideration how the clusters are distributed in Integrals of Motion space, this allows us to group clusters into substructures, and to compare substructures with one another.Results. We find that the 67 clusters identified by our algorithm can be merged into 12 extended substructures, while eight small clusters remain as such. The large substructures include the previously known Gaia-Enceladus, Helmi streams, Sequoia, and Thamnos 1 and 2. We identify a few overdensities that can be associated with the hot thick disc and which host a small metal-poor population. Especially notable is the largest (by number of member stars) substructure in our sample which, although peaking at the metallicity characteristic of the thick disk has a very well populated metal-poor component, and dynamics intermediate between the hot thick disc and the halo. We also identify additional debris in the region occupied by Sequoia with clearly distinct kinematics. Although only a small subset of the stars in our sample have chemical abundance information, we are able to identify different trends of [Mg/Fe] vs [Fe/H] for the various substructures confirming our dissection of the nearby halo. Conclusions. We find that at least 20% of the halo near the Sun is associated to substructures. When comparing their global properties, we note that those substructures on retrograde orbits are not only more metal-poor on average but also older. We provide a table summarising the properties of the substructures, as well as a membership list that can be used, for example, for follow-up chemical abundance studies.

Substructure in the stellar halo near the Sun. I. Data-driven clustering in Integrals of Motion space

“…Inspection of the hierarchical tree describing our data set reveals a complex web of relationships between the significant clusters, suggesting that they can be tentatively grouped into at least 6 main large structures, many of which can be associated to previously identified halo substructures, and a number of independent substructures. This preliminary conclusion is further explored in an accompanying paper by Ruiz-Lara et al (2022), where we also characterize the substructures in terms of their stellar populations. Conclusions.…”

“…Two of our significant clusters (#62 and #63) that have similar L z and E n as Arjuna/Sequoia/I'itoi, while another cluster (#64) clearly has different L z and E n , although also satisfies a more generous L z − E n selection criterion for retrograde structures by Naidu et al (2020). We investigate the relationship between all these clusters in detail in Ruiz-Lara et al (2022).…”

“…To fully assess the tentative division of our halo set into 6 main groups, as well as to study the significance and independence of the finer structures within the groups, would require a detailed analysis of the internal properties of these clusters and substructures, such as their stellar populations, metallicities, and chemical abundances. We defer such an exhaustive analysis to a separate paper (Ruiz-Lara et al 2022). Nonetheless, as a first example, we here focus our attention on the pair of clusters #60 and #61, identified as substructure E, that are likely part of the Helmi streams .…”

“…When it comes to machine learning and advanced clustering algorithms, commonly the results are hard to interpret, especially in the case of unsupervised machine learning where there are no ground truth labels. Although training and testing is possible via the use of numerical simulations (see Sanderson et al 2020;Ostdiek et al 2020), such simulations have many limitations themselves and there is no guarantee that the results obtained can be extrapolated to real data sets. Furthermore, the vast majority of clustering algorithms require a selection of parameters which have a deterministic impact on the results (see e.g.…”

“…The first conclusions of our study are summarised in Section 6. In paper II (Ruiz-Lara et al 2022) we focus on the characterization of the properties of the structures identified in this paper.…”