SIMBAD references

2022MNRAS.513.2509C - Mon. Not. R. Astron. Soc., 513, 2509-2521 (2022/June-3)

Tracing Milky Way substructure with an RR Lyrae hierarchical clustering forest.

COOK B.T., WOODS D.F., RUPRECHT J.D., VAREY J., MASTANDREA R., DE SOTO K., HARBURG J.F., REBBAPRAGADA U. and MAHABAL A.A.

Abstract (from CDS):

RR Lyrae variable stars have long been reliable standard candles used to discern structure in the Local Group. With this in mind, we present a routine to identify groupings containing a statistically significant number of RR Lyrae variables in the Milky Way environment. RR Lyrae variable groupings, or substructures, with potential Galactic archaeology applications are found using a forest of agglomerative, hierarchical clustering trees, whose leaves are Milky Way RR Lyrae variables. Each grouping is validated by ensuring that the internal RR Lyrae variable proper motions are sufficiently correlated. Photometric information was collected from the Gaia second data release and proper motions from the (early) third data release. After applying this routine to the catalogue of 91 234 variables, we are able to report 16 unique RR Lyrae substructures with physical sizes of less than 1 kpc. Five of these substructures are in close proximity to Milky Way globular clusters with previously known tidal tails and/or a potential connection to Galactic merger events. One candidate substructure is in the neighbourhood of the Large Magellanic Cloud but is more distant (and older) than known satellites of the dwarf galaxy. Our study ends with a discussion of ways in which future surveys could be applied to the discovery of Milky Way stellar streams.

Abstract Copyright: © 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society

Journal keyword(s): catalogues - stars: variables: RR Lyrae - Galaxy: halo - Galaxy: stellar content - Galaxy: structure

Status at CDS : Examining the need for a new acronym. // Large table(s) will be appraised for possible ingestion in VizieR.

Simbad objects: 40

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