Identifying stellar streams in Gaia DR2 with data mining techniques

Author:

Borsato Nicholas W1ORCID,Martell Sarah L12ORCID,Simpson Jeffrey D1ORCID

Affiliation:

1. School of Physics, UNSW Sydney, Sydney, NSW 2052, Australia

2. ARC Centre of Excellence for All Sky Astrophysics in Three Dimensions (ASTRO-3D), Australia

Abstract

ABSTRACT Streams of stars from captured dwarf galaxies and dissolved globular clusters are identifiable through the similarity of their orbital parameters, a fact that remains true long after the streams have dispersed spatially. We calculate the integrals of motion for 31 234 stars, to a distance of 4 kpc from the Sun, which have full and accurate 6D phase space positions in the Gaia DR2 catalogue. We then apply a novel combination of data mining, numerical, and statistical techniques to search for stellar streams. This process returns five high confidence streams (including one which was previously undiscovered), all of which display tight clustering in the integral of motion space. Colour–magnitude diagrams indicate that these streams are relatively simple, old, metal-poor populations. One of these resolved streams shares very similar kinematics and metallicity characteristics with the Gaia-Enceladus dwarf galaxy remnant, but with a slightly younger age. The success of this project demonstrates the usefulness of data mining techniques in exploring large data sets.

Funder

European Space Agency

Australian Research Council

University of New South Wales

National Aeronautics and Space Administration

Jet Propulsion Laboratory

California Institute of Technology

Publisher

Oxford University Press (OUP)

Subject

Space and Planetary Science,Astronomy and Astrophysics

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