Tracing Milky Way substructure with an RR Lyrae hierarchical clustering forest
Abstract
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.
Additional Information
© 2022 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Accepted 2022 April 7. Received 2022 April 6; in original form 2021 July 22. Published: 20 April 2022. This material is based upon work supported by the United States Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force. The authors wish to thank Anthony Brown, Marta Reina-Campos, and the anonymous referee for insightful comments that helped improve the manuscript. BTC, KDS, and RM were supported by the Summer Research Program at MIT Lincoln Laboratory. BTC would like to thank the MIT Lincoln Laboratory staff, whose support during the internship made this work possible. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. Data Availability Statement: The data underlying this article are publicly available; they can be found using the citations and footnotes provided throughout the article. The article's online supplementary material includes a collection of this public data in a form that is well suited for this study.Attached Files
Published - stac1007.pdf
Accepted Version - 2204.05868.pdf
Supplemental Material - stac1007_supplemental_file.zip
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Additional details
- Eprint ID
- 115680
- Resolver ID
- CaltechAUTHORS:20220719-370083500
- FA8702-15-D-0001
- Air Force Office of Scientific Research (AFOSR)
- Massachusetts Institute of Technology (MIT)
- NASA/JPL/Caltech
- Gaia Multilateral Agreement
- Created
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2022-07-20Created from EPrint's datestamp field
- Updated
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2022-07-20Created from EPrint's last_modified field