ORCID Profile
0000-0002-9419-3725
Current Organisation
Universitat de Barcelona
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Publisher: EDP Sciences
Date: 2023
DOI: 10.1051/0004-6361/202244784
Abstract: Interpreting and modelling astronomical catalogues requires an understanding of the catalogues’ completeness or selection function: what properties determine an object’s probability of being including in the catalogue? Here we set out to empirically quantify the completeness of the overall catalogue of Gaia ’s third data release (DR3). This task is not straightforward because Gaia is the all-sky optical survey with the highest angular resolution to date and no consistent ground truth exists to allow direct comparisons. However, well-characterised deeper imaging enables an empirical assessment of Gaia ’s G -band completeness across parts of the sky. On this basis, we devised a simple analytical completeness model of Gaia as a function of the observed G magnitude and position over the sky, which accounts for both the effects of crowding and the complex Gaia scanning law. Our model only depends on a single quantity: the median magnitude M 10 in a patch of the sky of catalogued sources with astrometric_matched_transits ≤10. We note that M 10 reflects elementary completeness decisions in the Gaia pipeline and is computable from the Gaia DR3 catalogue itself and therefore applicable across the whole sky. We calibrated our model using the Dark Energy Camera Plane Survey (DECaPS) and tested its predictions against Hubble Space Telescope observations of globular clusters. We found that our model predicts Gaia ’s completeness values to a few per cent (RMS) across the sky. We make the model available as a part of the gaiaunlimited Python package built and maintained by the GaiaUnlimited project.
Publisher: EDP Sciences
Date: 06-2023
DOI: 10.1051/0004-6361/202243797
Abstract: Context. With the most recent Gaia data release, the number of sources with complete 6D phase space information (position and velocity) has increased to well over 33 million stars, while stellar astrophysical parameters are provided for more than 470 million sources, and more than 11 million variable stars are identified. Aims. Using the astrophysical parameters and variability classifications provided in Gaia DR3, we selected various stellar populations to explore and identify non-axisymmetric features in the disc of the Milky Way in configuration and velocity space. Methods. Using more about 580 000 sources identified as hot OB stars, together with 988 known open clusters younger than 100 Myr, we mapped the spiral structure associated with star formation 4−5 kpc from the Sun. We selected over 2800 Classical Cepheids younger than 200 Myr that show spiral features extending as far as 10 kpc from the Sun in the outer disc. We also identified more than 8.7 million sources on the red giant branch (RGB), of which 5.7 million have line-of-sight velocities. This later s le allows the velocity field of the Milky Way to be mapped as far as 8 kpc from the Sun, including the inner disc. Results. The spiral structure revealed by the young populations is consistent with recent results using Gaia EDR3 astrometry and source lists based on near-infrared photometry, showing the Local (Orion) Arm to be at least 8 kpc long, and an outer arm consistent with what is seen in HI surveys, which seems to be a continuation of the Perseus arm into the third quadrant. The subset of RGB stars with velocities clearly reveals the large-scale kinematic signature of the bar in the inner disc, as well as evidence of streaming motions in the outer disc that might be associated with spiral arms or bar resonances. A local comparison of the velocity field of the OB stars reveals similarities and differences with the RGB s le. Conclusions. This cursory study of Gaia DR3 data shows there is a rich bounty of kinematic information to be explored more deeply, which will undoubtedly lead us to a clearer understanding of the dynamical nature of the non-axisymmetric structures of the Milky Way.
Publisher: EDP Sciences
Date: 03-2020
DOI: 10.1051/0004-6361/201937386
Abstract: Context. Open clusters are key targets for studies of Galaxy structure and evolution, and stellar physics. Since the Gaia data release 2 (DR2), the discovery of undetected clusters has shown that previous surveys were incomplete. Aims. Our aim is to exploit the Big Data capabilities of machine learning to detect new open clusters in Gaia DR2, and to complete the open cluster s le to enable further studies of the Galactic disc. Methods. We use a machine-learning based methodology to systematically search the Galactic disc for overdensities in the astrometric space and identify the open clusters using photometric information. First, we used an unsupervised clustering algorithm, DBSCAN, to blindly search for these overdensities in Gaia DR2 ( l , b , ϖ , μ α * , μ δ ), and then we used a deep learning artificial neural network trained on colour–magnitude diagrams to identify isochrone patterns in these overdensities, and to confirm them as open clusters. Results. We find 582 new open clusters distributed along the Galactic disc in the region | b | 20°. We detect substructure in complex regions, and identify the tidal tails of a disrupting cluster UBC 274 of ∼3 Gyr located at ∼2 kpc. Conclusions. Adapting the mentioned methodology to a Big Data environment allows us to target the search using the physical properties of open clusters instead of being driven by computational limitations. This blind search for open clusters in the Galactic disc increases the number of known open clusters by 45%.
Publisher: EDP Sciences
Date: 28-04-2021
DOI: 10.1051/0004-6361/202039588
Abstract: Context. This work is part of the Gaia Data Processing and Analysis Consortium papers published with the Gaia Early Data Release 3 (EDR3). It is one of the demonstration papers aiming to highlight the improvements and quality of the newly published data by applying them to a scientific case. Aims. We use the Gaia EDR3 data to study the structure and kinematics of the Magellanic Clouds. The large distance to the Clouds is a challenge for the Gaia astrometry. The Clouds lie at the very limits of the usability of the Gaia data, which makes the Clouds an excellent case study for evaluating the quality and properties of the Gaia data. Methods. The basis of our work are two s les selected to provide a representation as clean as possible of the stars of the Large Magellanic Cloud (LMC) and the Small Magellanic Cloud (SMC). The selection used criteria based on position, parallax, and proper motions to remove foreground contamination from the Milky Way, and allowed the separation of the stars of both Clouds. From these two s les we defined a series of subs les based on cuts in the colour-magnitude diagram these subs les were used to select stars in a common evolutionary phase and can also be used as approximate proxies of a selection by age. Results. We compared the Gaia Data Release 2 and Gaia EDR3 performances in the study of the Magellanic Clouds and show the clear improvements in precision and accuracy in the new release. We also show that the systematics still present in the data make the determination of the 3D geometry of the LMC a difficult endeavour this is at the very limit of the usefulness of the Gaia EDR3 astrometry, but it may become feasible with the use of additional external data. We derive radial and tangential velocity maps and global profiles for the LMC for the several subs les we defined. To our knowledge, this is the first time that the two planar components of the ordered and random motions are derived for multiple stellar evolutionary phases in a galactic disc outside the Milky Way, showing the differences between younger and older phases. We also analyse the spatial structure and motions in the central region, the bar, and the disc, providing new insightsinto features and kinematics. Finally, we show that the Gaia EDR3 data allows clearly resolving the Magellanic Bridge, and we trace the density and velocity flow of the stars from the SMC towards the LMC not only globally, but also separately for young and evolved populations. This allows us to confirm an evolved population in the Bridge that is slightly shift from the younger population. Additionally, we were able to study the outskirts of both Magellanic Clouds, in which we detected some well-known features and indications of new ones.
No related grants have been discovered for Alfred Castro-Ginard.