J/A+A/672/A65ivo://CDS.VizieR/J/A+A/672/A65doi:10.26093/cds/vizier.36720065CDSJimenez-Arranz O.Romero-Gomez M.Luri X.Masana E.2023-10-16T11:56:31Z2023-03-30T10:09:06ZCDS support team

CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France

cds-question@unistra.frmagellanic-cloudsastronomical-modelsPrevious attempts to separate Small Magellanic Cloud (SMC) stars from the Milky Way (MW) foreground stars are based only on the proper motions of the stars. In this paper, we aim to develop a statistical classification technique to effectively separate the SMC stars from the MW stars using a wider set of Gaia data. We aim to reduce the possible contamination from MW stars compared to previous strategies. The new strategy is based on a neural network classifier, applied to the bulk of the Gaia DR3 data. We produce three samples of stars flagged as SMC members, with varying levels of completeness and purity, obtained by application of this classifier. Using different test samples, we validated these classification results and compared them with the results of the selection technique employed in the Gaia Collaboration papers, which was based solely on the proper motions. The contamination of the MW in each of the three SMC samples is estimated to be in the 10-40% range; the "best case" in this range is obtained for bright stars (G<16), which belong to the V los sub-samples, and the "worst case" for the full SMC sample determined by using very stringent criteria based on StarHorse distances. A further check based on the comparison with a nearby area with uniform sky density indicates that the global contamination in our samples is probably close to the low end of the range, around 10%. We provide three selections of SMC star samples with different degrees of purity and completeness, for which we estimate a low contamination level and which we have successfully validated using SMC RR Lyrae, SMC Cepheids, and SMC-MW StarHorse samples.2023A&A...672A..65Jhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/672/A65CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toI/355 : Gaia DR3 Part 1. Main source (Gaia Collaboration, 2022)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/672/A65https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/672/A65http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/672/A65https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/672/A65https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/672/A65http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/672/A65GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/A+A/672/A65/class (SMC/MW classification)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/A+A/672/A65/class?https://vizier.iucaa.in/viz-bin/conesearch/J/A+A/672/A65/class?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/A+A/672/A65/class?GETtext/xml+votable180.050000true1.3536372-74.50902830.0055555555555555563/514 520 4/2062 2084 2088-2090 2821 2832-2833 2836 5/8203 8206-8207 8240 8242-8243 8254 8344-8346 8364-8366 8368 8704-8705 11281 11283 11293 11336-11337 11340-11341 11348-11350 11520 6/32811 32874-32875 32964 32966-32967 32984 32986 33008 33010-33011 33360-33362 33368 33370 33388-33390 33392 33468 33476 33480-33482 34824-34825 34832 45121 45123 45129 45157 45168-45169 45171 45404-45405 45408-45409 45412 46084https://cdsarc.cds.unistra.fr/viz-bin/moc/J/A+A/672/A65?format=asciidefaultJ/A+A/672/A65/classSMC/MW classification_RAPositions from Gaia DR3 (Cat. I/355) (right ascension part)degpos.eq.ra;meta.mainindexed_DEPositions from Gaia DR3 (Cat. I/355) (declination part)degpos.eq.dec;meta.mainindexedrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintGaiaDR3Gaia DR3 source_idmeta.id;meta.mainP[0/1] Probability of being a SMC/MW star (1)stat.fit.goodnessdouble