Substructure in the stellar halo near the Sun

Abstract

Context. Merger debris is expected to populate the stellar haloes of galaxies. In the case of the Milky Way, this debris should be apparent as clumps in a space defined by the orbital integrals of motion of the stars. Aims. Our aim is to develop a data-driven and statistics-based method for finding these clumps in integrals-of-motion space for nearby halo stars and to evaluate their significance robustly. Methods. We used data from Gaia EDR3, extended with radial velocities from ground-based spectroscopic surveys, to construct a sample of halo stars within 2.5 kpc from the Sun. We applied a hierarchical clustering method that makes exhaustive use of the single linkage algorithm in three-dimensional space defined by the commonly used integrals of motion energy E, together with two components of the angular momentum, Lz and L⊥. To evaluate the statistical significance of the clusters, we compared the density within an ellipsoidal region centred on the cluster to that of random sets with similar global dynamical properties. By selecting the signal at the location of their maximum statistical significance in the hierarchical tree, we extracted a set of significant unique clusters. By describing these clusters with ellipsoids, we estimated the proximity of a star to the cluster centre using the Mahalanobis distance. Additionally, we applied the HDBSCAN clustering algorithm in velocity space to each cluster to extract subgroups representing debris with different orbital phases. Results. Our procedure identifies 67 highly significant clusters (> 3σ), containing 12% of the sources in our halo set, and 232 subgroups or individual streams in velocity space. In total, 13.8% of the stars in our data set can be confidently associated with a significant cluster based on their Mahalanobis distance. Inspection of the hierarchical tree describing our data set reveals a complex web of relations between the significant clusters, suggesting that they can be tentatively grouped into at least six main large structures, many of which can be associated with previously identified halo substructures, and a number of independent substructures. This preliminary conclusion is further explored in a companion paper, in which we also characterise the substructures in terms of their stellar populations. Conclusions. Our method allows us to systematically detect kinematic substructures in the Galactic stellar halo with a data-driven and interpretable algorithm. The list of the clusters and the associated star catalogue are provided in two tables available at the CDS.

Two-point Correlation Function Studies for the Milky Way: Discovery of Spatial Clustering from Disk Excitations and Substructure

“…Regardless of whether the observed perturbations come from a superposition of waves (Bland-Hawthorn & Tepper-García 2021) or from a Sagittarius impact modulated by the Large Magellanic Cloud (Laporte et al 2018), it seems increasingly clear that a multitude of effects are likely recorded in the structure of the Milky Way-and this is made all the more likely by the growing census of past mergers (Lövdal et al 2022;Malhan et al 2022). If the Milky Way did grow hierarchically over cosmic time, as expected in the cold dark matter paradigm (Peebles 1993), the aggregate of its entire merger history and its extremely long relaxation time (Binney & Tremaine 2008) does lend credence to a picture of multiple effects.…”

Unveiling the past evolution of the progenitor of the Helmi streams

“…We identified 646 candidate members of the Helmi streams in this 6D halo sample using the criteria from Koppelman et al (2019b), Dodd et al (2022), andLövdal et al (2022). Ideally, one would compute the SFH of the progenitor of the Helmi streams from this subset.…”

“…To each star in HelmiA, we assigned the v los of the closest star in the (µ l , µ b , l, b, distance) space from the 6D (Gaia) halo sample. We then computed their integrals of motion (L z , L ⊥ , and energy) and only kept stars with values compatible with belonging to the Helmi streams (following Dodd et al 2022;Lövdal et al 2022). By definition, the purity of this sample is 100% (as all the stars in HelmiA * will have both proper motions and line-of-sight velocities that make them true members of the Helmi streams 2 ).…”

“…The identification of stars linked to each of the MW building blocks is commonly done based on the dynamical properties of halo stars, such as integrals of motion or action space (Helmi & de Zeeuw 2000;Myeong et al 2018;Lövdal et al 2022). In some cases, chemical information is added (Naidu et al 2020;Ruiz-Lara et al 2022).…”

Substructure in the stellar halo near the Sun

“…To reach these goals, statistically robust identification of the various substructures that may be associated to merger debris is necessary, as well as reliable determination of the likelihood of stars to belong to any of the substructures. In this work, we use the outcome of a data-driven hierarchical clustering algorithm (see Lövdal et al 2022, Paper I from now on) to this end. In Paper I, we identified 67 highly significant clusters in IoM space for nearby stars with halo-like kinematics.…”