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      Painting a portrait of the Galactic disc with its stellar clusters

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          Abstract

          Context.The large astrometric and photometric survey performed by the Gaiamission allows for a panoptic view of the Galactic disc and its stellar cluster population. Hundreds of stellar clusters were only discovered after the latest Gaiadata release (DR2) and have yet to be characterised.

          Aims.Here we make use of the deep and homogeneous Gaiaphotometry down to G= 18 to estimate the distance, age, and interstellar reddening for about 2000 stellar clusters identified with GaiaDR2 astrometry. We use these objects to study the structure and evolution of the Galactic disc.

          Methods.We relied on a set of objects with well-determined parameters in the literature to train an artificial neural network to estimate parameters from the Gaiaphotometry of cluster members and their mean parallax.

          Results.We obtain reliable parameters for 1867 clusters. Our catalogue confirms the relative lack of old stellar clusters in the inner disc (with a few notable exceptions). We also quantify and discuss the variation of scale height with cluster age, and we detect the Galactic warp in the distribution of old clusters.

          Conclusions.This work results in a large and homogeneous cluster catalogue, allowing one to trace the structure of the disc out to distances of ∼4 kpc. However, the present sample is still unable to trace the outer spiral arm of the Milky Way, which indicates that the outer disc cluster census might still be incomplete.

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Astronomy & Astrophysics
                A&A
                EDP Sciences
                0004-6361
                1432-0746
                August 2020
                July 28 2020
                August 2020
                : 640
                : A1
                Article
                10.1051/0004-6361/202038192
                e25b7d4e-1b77-4507-8c0b-a09e70d694c3
                © 2020

                https://www.edpsciences.org/en/authors/copyright-and-licensing

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