Full 5D characterisation of the Sagittarius stream with Gaia DR2 RR Lyrae

Context. The Sagittarius (Sgr) stream is one of the best tools that we currently have to estimate the mass and shape of our Galaxy. However, assigning membership and obtaining the phase-space distribution of the stars that form the tails of the stream is quite challenging. Aims. Our goal is to produce a catalogue of the RR Lyrae stars of Sgr and obtain an empiric measurement of the trends along the stream in sky position, distance, and tangential velocity. Methods. We generated two initial samples from the Gaia DR2 RR Lyrae catalogue: one selecting only the stars within ±20° of the orbital plane of Sagittarius (Strip), and the other resulting from application of the Pole Count Map (nGC3) algorithm. We then used the model-independent, deterministic method developed in this work to remove most of the contamination by detecting and isolating the stream in distance and proper motions. Results. The output is two empiric catalogues: the Strip sample (higher-completeness, lower-purity) which contains 11 677 stars, and the nGC3 sample (higher-purity, lower-completeness) with 6608 stars. We characterise the changes along the stream in all the available dimensions, namely the five astrometric dimensions plus the metallicity, covering more than 2π rad in the sky, and obtain new estimates for the apocentres and the mean [Fe/H] of the RR Lyrae population. Also, we show the first map of the two components of the tangential velocity thanks to the combination of distances and proper motions. Finally, we detect the bifurcation in the leading arm and report no significant difference between the two branches in terms of metallicity, kinematics, or distance. Conclusions. We provide the largest sample of RR Lyrae candidates of Sgr, which can be used as input for a spectroscopic follow-up or as a reference for the new generation of models of the stream through the interpolators in distance and velocity that we constructed.Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This project has received funding from the University of Barcelona’s official doctoral program for the development of a R+D+i project under the APIF grant and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 745617. This work was supported by the MINECO (Spanish Ministry of Economy) through grants ESP2016-80079-C2-1-R (MINECO/FEDER, UE) and ESP2014-55996-C2-1-R (MINECO/FEDER, UE) and MDM-2014-0369 of ICCUB (Unidad de Excelencia “María de Maeztu”). This project has received support from the DGAPA/UNAM PAPIIT program grant IG100319. The work reported on in this publication has been fully or partially supported by COST Action CA18104: MW-Gaia. CM acknowledges support from the ICC University of Barcelona visiting academic grants and thanks the Gaia UB team for hosting her during part of this research