Search

The 4MOST Stellar Clusters Survey will target essentially all the Galactic globular and open clusters and star-forming regions accessible to 4MOST (about 120 globulars, 1800 open clusters and 80 star-forming regions). This will: shed light on how clusters form, evolve, dissolve, and populate the Milky Way; calibrate complex physics that affects stellar evolution, on which our ability to measure accurate ages ultimately rests; and evaluate the contribution of star clusters to the formation and evolution of the individual Galactic components with unparalleled statistics., Published in The Messenger vol. 190, pp. 13-16, March 2023.

The huge improvements in the precision of the published data in Gaia EDR3, particularly for parallaxes and proper motions, has given a push to the detection of new open clusters in the Milky Way. In this talk, I will revise our methodology to search for open clusters, and how it has been adapted to a Big Data environment to analyse hundreds of millions of stars looking for relations and patterns among them. The application of the method to Gaia EDR3 has resulted in the discovery of 664 new open clusters, which, added to the 646 found in our previous searches in Gaia DR2, represent about 50% of the known open cluster population. I will also revise how this updated open cluster catalogue, with estimated astrophysical parameters, can provide insights about the structure and evolution of our Galaxy, particularly focusing on the spiral arms.

[eng] Open clusters are groups of stars, gravitationally bound together, that were born from the same molecular cloud and, thus, share similar positions, kinematics, ages and metallicities. Traditional methods to detect open clusters rely in the visual inspection of regions of the sky to look for positional overdensities of stars, which then are checked to follow an isochrone pattern in a colour-magnitude diagram. The publication of the second Gaia data release, with more than 1.3 billion stars with parallax and proper motion measurements together with mean photometry in three broadbands, boosted the development of novel machine learning-based techniques to automatise the search for open clusters, using both the astrometric and photometric information. The characterised open clusters in the Galaxy are popular tracers of properties of the Galactic disc such as the structure and evolution of the spiral arms, or testbed for stellar evolution studies for instance, because their astrophysical parameters are estimated with greater precision than for field stars. Therefore, a good understanding of the open cluster population in the Milky Way is key for Galactic archaeology studies. Our aim for this thesis is to transform classical methodologies to detect different kinds of patterns from astronomical data, that mostly relies on visual inspection, to an automatic data mining procedure to extract meaningful information from stellar catalogues. We also aim to use the result of the application of machine learning techniques to Gaia data, in a broader Galactic context. We have developed a data mining methodology to blindly search for open clusters in the Galactic disc. First, we use a density-based clustering algorithm, DBSCAN, to search for overdensities in the five-dimensional astrometric parameter space in Gaia data. The deployment of the clustering step in a Big Data environment, at the MareNostrum supercomputer located in the Barcelona Supercomputing Center, prevents the search to be limited by computational limitations. Second, the detected overdensities are classified into mere statistical or physical overdensities using an artificial neural network trained to recognise the isochrone pattern that open cluster member stars follow in a colour-magnitude diagram. We estimate astrophysical parameters such as ages, distances and line-of-sight extinctions for the whole open cluster population using an artificial neural network trained on well-known open clusters. We use this additional information, together with radial velocities gathered from different space-based and ground-based surveys, to trace the Galactic spiral present-day structure using GaussianMixtureModels to associate the young (< 30 Myr) open clusters to their mother spiral arms. We also describe the spiral arms evolution during the last 80 Myr to provide new insights into the nature of the Milky Way spiral structure. The automatization of the open cluster detection procedure, together with its deployment in a Big Data environment, has resulted in more than 650 new open clusters detected with this methodology. The new UBC clusters (named after the University of Barcelona) represent one-third of the actual open clusters census (2017 objects with Gaia DR2 parameters), and it is the largest single contribution to the open cluster catalogue. We are able to add 264 young open clusters (< 30 Myr) to the 84 high-mass star- forming regions traditionally used to trace spiral arms, to increase the Galactocentric azimuth range where the Milky Way spiral arms are defined, and better estimate their present-day parameters. By analysing the age distribution of the open clusters across the Galactic spiral arms, and computing the spiral arms pattern speeds following the open clusters orbits from their birthplaces, we are able to disfavour classical density waves as the main mechanism for the formation of the Milky Way spiral arms, favouring a transient behaviour. This thesis has shown that the use of machine learning, with proper treatment of the computational resources, has a long journey ahead in a data-dominated future for Astronomy., [cat] Els cúmuls estel·lars oberts són conjunts d'estels, lligats gravitatòriament, nascuts al mateix núvol molecular que tenen propietats similars. Aquests cúmuls són traçadors populars de la estructura del disc Galàctic, com ara els braços espirals. El segon llançament de dades de Gaia, amb més de 1300 milions d'estels, impossibilita la detecció de cúmuls a partir de mètodes tradicionals degut al gran volum del catàleg. Per això, el desenvolupament de tècniques automàtiques per aquest fi ha crescut juntament amb el volums dels catàlegs a analitzar. Hem desenvolupat una metodologia per a la cerca a cegues de cúmuls oberts al disc Galàctic. Hem utilitzat un algoritme de clustering, DBSCAN, per trobar sobredensitats en l'espai astromètric de cinc dimensions de Gaia. La implementació del mètode de clustering a un entorn de Big Data, al superordinador MareNostrum, ens permet cercar cúmuls oberts basant-nos en les seves propietats físiques. Les sobredensitats detectades s'identifiquen com a cúmuls oberts reals per mitjà d'una xarxa neuronal artificial que reconeix isòcrones en un diagrama de color-magnitud. L'automatització del procediment de detecció amb l'ús de tècniques de Big Data, ha resultat en més de 650 nous cúmuls. Aquests nous cúmul representen un terç de la població actual, i és la contribució individual més gran al catàleg. Hem pogut estimar les propietats físiques dels cúmuls com distància, edat i extinció, fent servir una xarxa neuronal artificial entrenada sobre cúmuls coneguts. Fem servir aquesta informació, juntament amb mesures de velocitat radial, per traçar l'estructura espiral actual de la nostra Galàxia associant els cúmuls oberts més joves (< 30 milions d'anys) al braç espiral on s'han format. Amb això, hem augmentat el nombre de traçadors de braços espirals, afegint 264 cúmuls joves als traçadors utilitzats tradicionalment. Això ens ha permès estimar millor els paràmetres actuals d'aquests braços. Analitzant la distribució en edat dels cúmuls dins dels braços espirals, i calculant la velocitat en la que aquests braços es mouen a partir de l'orbita dels cúmuls, hem pogut desfavorir la teoria clàssica d'ona de densitat com a mecanisme principal de formació de l'estructura espiral, trobant un comportament més transitori dels braços.

We present ~120,000 Spitzer/IRAC candidate young stellar objects (YSOs) based on surveys of the Galactic midplane between l~255 deg and 110 deg, including the GLIMPSE I, II, and 3D, Vela-Carina, Cygnus X, and SMOG surveys (613 square degrees), augmented by near-infrared catalogs. We employed a classification scheme that uses the flexibility of a tailored statistical learning method and curated YSO datasets to take full advantage of IRAC's spatial resolution and sensitivity in the mid-infrared ~3-9 micron range. Multi-wavelength color/magnitude distributions provide intuition about how the classifier separates YSOs from other red IRAC sources and validate that the sample is consistent with expectations for disk/envelope-bearing pre-main-sequence stars. We also identify areas of IRAC color space associated with objects with strong silicate absorption or polycyclic aromatic hydrocarbon emission. Spatial distributions and variability properties help corroborate the youthful nature of our sample. Most of the candidates are in regions with mid-IR nebulosity, associated with star-forming clouds, but others appear distributed in the field. Using Gaia DR2 distance estimates, we find groups of YSO candidates associated with the Local Arm, the Sagittarius-Carina Arm, and the Scutum-Centaurus Arm. Candidate YSOs visible to the Zwicky Transient Facility tend to exhibit higher variability amplitudes than randomly selected field stars of the same magnitude, with many high-amplitude variables having light-curve morphologies characteristic of YSOs. Given that no current or planned instruments will significantly exceed IRAC's spatial resolution while possessing its wide-area mapping capabilities, Spitzer-based catalogs such as ours will remain the main resources for mid-infrared YSOs in the Galactic midplane for the near future., Published in ApJS. 42 pages, 3 tables, and 28 figures (including 4 figure sets). Some column names in Table 1 have been modified to match the published version, but data remain unchanged. For convenience, copies of the tables can be accessed at https://sites.astro.caltech.edu/~mkuhn/SPICY/

Brown, A., et al. (Gaia Collaboration). This article has an erratum: [https://doi.org/10.1051/0004-6361/202039657e] [Context] We present the early installment of the third Gaia data release, Gaia EDR3, consisting of astrometry and photometry for 1.8 billion sources brighter than magnitude 21, complemented with the list of radial velocities from Gaia DR2. [Aims] A summary of the contents of Gaia EDR3 is presented, accompanied by a discussion on the differences with respect to Gaia DR2 and an overview of the main limitations which are present in the survey. Recommendations are made on the responsible use of Gaia EDR3 results. [Methods] The raw data collected with the Gaia instruments during the first 34 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium and turned into this early third data release, which represents a major advance with respect to Gaia DR2 in terms of astrometric and photometric precision, accuracy, and homogeneity. Results. Gaia EDR3 contains celestial positions and the apparent brightness in G for approximately 1.8 billion sources. For 1.5 billion of those sources, parallaxes, proper motions, and the (GBP - GRP) colour are also available. The passbands for G, GBP, and GRP are provided as part of the release. For ease of use, the 7 million radial velocities from Gaia DR2 are included in this release, after the removal of a small number of spurious values. New radial velocities will appear as part of Gaia DR3. Finally, Gaia EDR3 represents an updated materialisation of the celestial reference frame (CRF) in the optical, the Gaia-CRF3, which is based solely on extragalactic sources. The creation of the source list for Gaia EDR3 includes enhancements that make it more robust with respect to high proper motion stars, and the disturbing effects of spurious and partially resolved sources. The source list is largely the same as that for Gaia DR2, but it does feature new sources and there are some notable changes. The source list will not change for Gaia DR3. [Conclusions] Gaia EDR3 represents a significant advance over Gaia DR2, with parallax precisions increased by 30 per cent, proper motion precisions increased by a factor of 2, and the systematic errors in the astrometry suppressed by 30-40% for the parallaxes and by a factor ~2.5 for the proper motions. The photometry also features increased precision, but above all much better homogeneity across colour, magnitude, and celestial position. A single passband for G, GBP, and GRP is valid over the entire magnitude and colour range, with no systematics above the 1% level The Gaia mission and data processing have financially been supported by, in alphabetical order by country: the Algerian Centre de Recherche en Astronomie, Astrophysique et Géophysique of Bouzareah Observatory; the Austrian Fonds zur Förderung der wissenschaftlichen Forschung (FWF) Hertha Firnberg Programme through grants T359, P20046, and P23737; the BELgian federal Science Policy Office (BELSPO) through various PROgramme de Développement d’Expériences scientifiques (PRODEX) grants and the Polish Academy of Sciences – Fonds Wetenschappelijk Onderzoek through grant VS.091.16N, and the Fonds de la Recherche Scientifique (FNRS); the Brazil-France exchange programmes Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Coordenação de Aperfeicoamento de Pessoal de Nível Superior (CAPES) – Comité Français d’Evaluation de la Coopération Universitaire et Scientifique avec le Brésil (COFECUB); the National Science Foundation of China (NSFC) through grants 11573054 and 11703065 and the China Scholarship Council through grant 201806040200; the Tenure Track Pilot Programme of the Croatian Science Foundation and the École Polytechnique Fédérale de Lausanne and the project TTP-2018-07-1171 “Mining the Variable Sky”, with the funds of the Croatian-Swiss Research Programme; the Czech-Republic Ministry of Education, Youth, and Sports through grant LG 15010 and INTER-EXCELLENCE grant LTAUSA18093, and the Czech Space Office through ESA PECS contract 98058; the Danish Ministry of Science; the Estonian Ministry of Education and Research through grant IUT40-1; the European Commission’s Sixth Framework Programme through the European Leadership in Space Astrometry (ELSA) Marie Curie Research Training Network (MRTN-CT-2006-033481), through Marie Curie project PIOF-GA-2009-255267 (Space AsteroSeismology & RR Lyrae stars, SAS-RRL), and through a Marie Curie Transfer-of-Knowledge (ToK) fellowship (MTKD-CT-2004-014188); the European Commission’s Seventh Framework Programme through grant FP7-606740 (FP7-SPACE-2013-1) for the Gaia European Network for Improved data User Services (GENIUS) and through grant 264895 for the Gaia Research for European Astronomy Training (GREAT-ITN) network; the European Research Council (ERC) through grants 320360 and 647208 and through the European Union’s Horizon 2020 research and innovation and excellent science programmes through Marie Skłodowska-Curie grant 745617 as well as grants 670519 (Mixing and Angular Momentum tranSport of massIvE stars – MAMSIE), 687378 (Small Bodies: Near and Far), 682115 (Using the Magellanic Clouds to Understand the Interaction of Galaxies), and 695099 (A sub-percent distance scale from binaries and Cepheids – CepBin); the European Science Foundation (ESF), in the framework of the Gaia Research for European Astronomy Training Research Network Programme (GREAT-ESF); the European Space Agency (ESA) in the framework of the Gaia project, through the Plan for European Cooperating States (PECS) programme through grants for Slovenia, through contracts C98090 and 4000106398/12/NL/KML for Hungary, and through contract 4000115263/15/NL/IB for Germany; the Academy of Finland and the Magnus Ehrnrooth Foundation; the French Centre National d’Etudes Spatiales (CNES), the Agence Nationale de la Recherche (ANR) through grant ANR-10-IDEX-0001-02 for the “Investissements d’avenir” programme, through grant ANR-15-CE31-0007 for project “Modelling the Milky Way in the Gaia era” (MOD4Gaia), through grant ANR-14-CE33-0014-01 for project “The Milky Way disc formation in the Gaia era” (ARCHEOGAL), and through grant ANR-15-CE31-0012-01 for project “Unlocking the potential of Cepheids as primary distance calibrators” (UnlockCepheids), the Centre National de la Recherche Scientifique (CNRS) and its SNO Gaia of the Institut des Sciences de l’Univers (INSU), the “Action Fédératrice Gaia” of the Observatoire de Paris, the Région de Franche-Comté, and the Programme National de Gravitation, Références, Astronomie, et Métrologie (GRAM) of CNRS/INSU with the Institut National Polytechnique (INP) and the Institut National de Physique nucléaire et de Physique des Particules (IN2P3) co-funded by CNES; the German Aerospace Agency (Deutsches Zentrum für Luft- und Raumfahrt e.V., DLR) through grants 50QG0501, 50QG0601, 50QG0602, 50QG0701, 50QG0901, 50QG1001, 50QG1101, 50QG1401, 50QG1402, 50QG1403, 50QG1404, and 50QG1904 and the Centre for Information Services and High Performance Computing (ZIH) at the Technische Universität (TU) Dresden for generous allocations of computer time; the Hungarian Academy of Sciences through the Lendület Programme grants LP2014-17 and LP2018-7 and through the Premium Postdoctoral Research Programme (L. Molnár), and the Hungarian National Research, Development, and Innovation Office (NKFIH) through grant KH_18-130405; the Science Foundation Ireland (SFI) through a Royal Society – SFI University Research Fellowship (M. Fraser); the Israel Science Foundation (ISF) through grant 848/16; the Agenzia Spaziale Italiana (ASI) through contracts I/037/08/0, I/058/10/0, 2014-025-R.0, 2014-025-R.1.2015, and 2018-24-HH.0 to the Italian Istituto Nazionale di Astrofisica (INAF), contract 2014-049-R.0/1/2 to INAF forthe Space Science Data Centre (SSDC, formerly known as the ASI Science Data Center, ASDC), contracts I/008/10/0, 2013/030/I.0, 2013-030-I.0.1-2015, and 2016-17-I.0 to the Aerospace Logistics Technology Engineering Company (ALTEC S.p.A.), INAF, and the Italian Ministry of Education, University, and Research (Ministero dell’Istruzione, dell’Università e della Ricerca) through the Premiale project “MIning The Cosmos Big Data and Innovative Italian Technology for Frontier Astrophysics and Cosmology” (MITiC); the Netherlands Organisation for Scientific Research (NWO) through grant NWO-M-614.061.414, through a VICI grant (A.H.), and through a Spinoza prize (A.H.), and the Netherlands Research School for Astronomy (NOVA); the Polish National Science Centre through HARMONIA grant 2018/06/M/ST9/00311, DAINA grant 2017/27/L/ST9/03221, and PRELUDIUM grant 2017/25/N/ST9/01253, and the Ministry of Science and Higher Education (MNiSW) through grant DIR/WK/2018/12; the Portugese Fundação para a Ciência e a Tecnologia (FCT) through grants SFRH/BPD/74697/2010 and SFRH/BD/128840/2017 and the Strategic Programme UID/FIS/00099/2019 for CENTRA; the Slovenian Research Agency through grant P1-0188; the Spanish Ministry of Economy (MINECO/FEDER, UE) through grants ESP2016-80079-C2-1-R, ESP2016-80079-C2-2-R, RTI2018-095076-B-C21, RTI2018-095076-B-C22, BES-2016-078499, and BES-2017-083126 and the Juan de la Cierva formación 2015 grant FJCI-2015-2671, the Spanish Ministry of Education, Culture, and Sports through grant FPU16/03827, the Spanish Ministry of Science and Innovation (MICINN) through grant AYA2017-89841P for project “Estudio de las propiedades de los fósiles estelares en el entorno del Grupo Local” and through grant TIN2015-65316-P for project “Computación de Altas Prestaciones VII”, the Severo Ochoa Centre of Excellence Programme of the Spanish Government through grant SEV2015-0493, the Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia “María de Maeztu”) through grants MDM-2014-0369 and CEX2019-000918-M, the University of Barcelona’s official doctoral programme for the development of an R+D+i project through an Ajuts de Personal Investigador en Formació (APIF) grant, the Spanish Virtual Observatory through project AyA2017-84089, the Galician Regional Government, Xunta de Galicia, through grants ED431B-2018/42 and ED481A-2019/155, support received from the Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC) funded by the Xunta de Galicia, the Xunta de Galicia and the Centros Singulares de Investigación de Galicia for the period 2016-2019 through CITIC, the European Union through the European Regional Development Fund (ERDF) / Fondo Europeo de Desenvolvemento Rexional (FEDER) for the Galicia 2014-2020 Programme through grant ED431G-2019/01, the Red Española de Supercomputación (RES) computer resources at MareNostrum, the Barcelona Supercomputing Centre – Centro Nacional de Supercomputación (BSC-CNS) through activities AECT-2016-1-0006, AECT-2016-2-0013, AECT-2016-3-0011, and AECT-2017-1-0020, the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya through grant 2014-SGR-1051 for project “Models de Programació i Entorns d’Execució Parallels” (MPEXPAR), and Ramon y Cajal Fellowship RYC2018-025968-I; the Swedish National Space Agency (SNSA/Rymdstyrelsen); the Swiss State Secretariat for Education, Research, and Innovation through the Mesures d’Accompagnement, the Swiss Activités Nationales Complémentaires, and the Swiss National Science Foundation; the United Kingdom Particle Physics and Astronomy Research Council (PPARC), the United Kingdom Science and Technology Facilities Council (STFC), and the United Kingdom Space Agency (UKSA) through the following grants to the University of Bristol, the University of Cambridge, the University of Edinburgh, the University of Leicester, the Mullard Space Sciences Laboratory of University College London, and the United Kingdom Rutherford Appleton Laboratory (RAL): PP/D006511/1, PP/D006546/1, PP/D006570/1, ST/I000852/1, ST/J005045/1, ST/K00056X/1, ST/K000209/1, ST/K000756/1, ST/L006561/1, ST/N000595/1, ST/N000641/1, ST/N000978/1, ST/N001117/1, ST/S000089/1, ST/S000976/1, ST/S001123/1, ST/S001948/1, ST/S002103/1, and ST/V000969/1. This work made use of the following software: Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration 2013, 2018, http://www.astropy.org), IPython (Pérez & Granger 2007, https://ipython.org/), Jupyter (https://jupyter.org/), Matplotlib (Hunter 2007, https://matplotlib.org), SciPy (Virtanen et al. 2020, https://www.scipy.org), NumPy (Harris et al. 2020, https://numpy.org), and TOPCAT (Taylor 2005, http://www.starlink.ac.uk/topcat/). This work has made use of NASA’s Astrophysics Data System. We thank the referee, Andy Casey, for a careful reading of the manuscript.

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 sample 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, μα*, μδ), 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%.

In the time of massive surveys such as Gaia; machine learning (ML) has shown to be a powerful tool to explore the catalogue searching for hitherto unknown stellar structures. In particular; the combination of both unsupervised and supervised learning algorithms allows to significantly improve the detection of open clusters (OCs). We use a combination of ML techniques to systematically mine the Gaia archive; looking for overdensities in the five dimensional astrometric space (positions; parallax and proper motions). On the one hand; the DBSCAN algorithm identifies overdensities in this space that can correspond to physical clusters. On the other hand; the clusters found are then separated in physical clusters (OCs) and random statistical clusters using an artificial neural network (ANN) to recognise the isochrone pattern of the true OCs in the color magnitude diagram (CMD) of the member stars. With this methodology we were able to detect 23 previously unknown OCs in the TGAS dataset; and later confirm them using Gaia DR2 data. Using Gaia DR2; we explore a region near the galactic anticentre (120

Context. We present the second Gaiadata release, Gaia DR2, consisting of astrometry, photometry, radial velocities, and information on astrophysical parameters and variability, for sources brighter than magnitude 21. In addition epoch astrometry and photometry are provided for a modest sample of minor planets in the solar system. Aims. A summary of the contents of Gaia DR2 is presented, accompanied by a discussion on the differences with respect to Gaia DR1 and an overview of the main limitations which are still present in the survey. Recommendations are made on the responsible use of Gaia DR2 results. Methods. The raw data collected with the Gaia instruments during the first 22 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into this second data release, which represents a major advance with respect to Gaia DR1 in terms of completeness, performance, and richness of the data products. Results. Gaia DR2 contains celestial positions and the apparent brightness in G for approximately 1.7 billion sources. For 1.3 billion of those sources, parallaxes and proper motions are in addition available. The sample of sources for which variability information is provided is expanded to 0.5 million stars. This data release contains four new elements: broad-band colour information in the form of the apparent brightness in the GBP (330-680 nm) and GRP (630-1050 nm) bands is available for 1.4 billion sources; median radial velocities for some 7 million sources are presented; for between 77 and 161 million sources estimates are provided of the stellar effective temperature, extinction, reddening, and radius and luminosity; and for a pre-selected list of 14 000 minor planets in the solar system epoch astrometry and photometry are presented. Finally, Gaia DR2 also represents a new materialisation of the celestial reference frame in the optical, the Gaia-CRF2, which is the first optical reference frame based solely on extragalactic sources. There are notable changes in the photometric system and the catalogue source list with respect to Gaia DR1, and we stress the need to consider the two data releases as independent. Conclusions. Gaia DR2 represents a major achievement for the Gaia mission, delivering on the long standing promise to provide parallaxes and proper motions for over 1 billion stars, and representing a first step in the availability of complementary radial velocity and source astrophysical information for a sample of stars in the Gaia survey which covers a very substantial fraction of the volume of our galaxy.

Context. The Gaia spacecraft of the European Space Agency (ESA) has been securing observations of Solar System Objects since the beginning of its operations. The Data Release 2 (DR2) contains the observations of a selected sample of 14,099 Solar System Objects, SSOs for brevity. These asteroids were already identified and numbered by the Minor Planet Center repository. Positions are provided for each Gaia observation, at CCD level. As additional information, complementary to astrometry, the apparent brightness of SSOs in the unfiltered G band is also provided for selected observations. Aims. We explain the processing of SSO data, and the criteria used to select the sample published in the Gaia DR2. We then explore the data set to assess its quality. Methods. To exploit the main data product for the Solar System in Gaia DR2, namely epoch astrometry of asteroids, it is necessary to take into account the unusual properties of the uncertainty, as the position information is nearly one-dimensional. Once this aspect is appropriately handled, an orbit fit can be obtained, with post-fit residuals that are overall consistent with the a-priori error model used to define individual values of the astrometric uncertainty. The role of both random and systematic errors is described. The distribution of residuals allows us to identify possible contaminants in the data set (such as stars). Photometry in the G band is compared to computed values from reference asteroid shapes and to the flux registered, at the corresponding epochs, by the red and blue photometers (RP and BP). Results. The overall astrometric performance is close to the expectations, with an optimal range of brightness G 12-17. In that range the typical transit-level accuracy is well below 1 milliarcsec (mas). For fainter asteroids, the growing photon noise deteriorates the performance. Asteroids brighter than G = 12 are aff ected by a lower performance of the processing of their signals. The dramatic improvement brought by Gaia DR2 astrometry of SSOs is demonstrated by comparisons to the archive data, and by preliminary tests on the detection of subtle non-gravitational effects.

Context. The second release of Gaia data (Gaia DR2) contains the astrometric parameters for more than half a million quasars which define akinematically non-rotating reference frame in the optical domain. A subset of them have accurate VLBI positions which allow the axes of thereference frame to be aligned with the ICRF radio frame. Aims. We aim to describe the astrometric and photometric properties of the quasars selected to represent Gaia-CRF2, the celestial reference frame of Gaia DR2, and to compare the optical and radio positions for sources with accurate VLBI positions. Methods. Descriptive statistics are used to characterise the overall properties of the quasar sample. Residual rotation and orientation errors and large-scale systematics are quantified by means of expansions in vector spherical harmonics. Positional differences are calculated relative to a prototype version of the forthcoming ICRF3. Results. Gaia-CRF2 is materialised by the positions of a sample of 556 869 sources in Gaia DR2, obtained from a positional cross-match with the ICRF3-prototype and AllWISE AGN catalogues. The sample constitutes a clean, dense, and homogeneous set of extragalactic point sources in the magnitude range G ' 16 to 21 mag with accurately known optical positions. The median positional uncertainty is 0.12 mas for G < 18 mag and 0.5 mas at G = 20 mag. Large-scale systematics are estimated to be in the range 20 to 30 μas. The accuracy claims are supported by the parallaxes and proper motions of the quasars in Gaia DR2. The optical positions for a subset of 2820 sources in common with the ICRF3-prototype show very good overall agreement with the radio positions, but several tens of sources have significantly discrepant positions. Conclusions. Based on less than 40% of the data expected from the nominal Gaia mission, Gaia-CRF2 is the first realisation of a non-rotating global optical reference frame meeting the ICRS prescriptions, i.e. built only on extragalactic sources. In accuracy it matches the current radio frame realised in the ICRF but with a much higher density of sources in all parts of the sky except along the Galactic equator.

Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2022, Tutors: Carme Jordi Nebot, Alfred Castro-Ginard, With the arrival of the third Data Release from the Gaia mission, we receive new information that can be really helpful to detect new clusters in the Galactic disc. So far, cluster hunting methods used five parameters, which are position, parallax and proper motions of the stars, in order to identify clusters. The new release arrives with the mean radial velocity (RV ) measured for 33 million stars, which can be added as the sixth dimension in order to improve the efficiency of these methods. In this work, we implement a six-parameter detection method based on the work developed for previous releases. The method searches for clusters in a region of the sky with two input hyperparameters, the size of the box and the number of neighbour stars considered to form a cluster (L, minPts). We have run the algorithm for 81 different pairs, to determine which one performs better through several regions of the sky. The most efficient pair has been (L, minPts) = (13◦, 11), followed closely by (16◦, 12). Both had near 60% of the clusters found and 70% of correctly clustered stars while having a low number of field stars clustered, which means the results had lower noise.