J/MNRAS/477/894ivo://CDS.VizieR/J/MNRAS/477/894doi:10.26093/cds/vizier.74770894CDSAbraham S.Aniyan A.K.Kembhavi A.K.Philip N.S.Vaghmare K.2024-08-18T20:19:31Z2021-06-28T13:16:43ZCDS support team

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

cds-question@unistra.frsurveysgalaxiesgalaxy-classification-systemsWe present an automated method for the detection of bar structure in optical images of galaxies using a deep convolutional neural network that is easy to use and provides good accuracy. In our study, we use a sample of 9346 galaxies in the redshift range of 0.009-0.2 from the Sloan Digital Sky Survey (SDSS), which has 3864 barred galaxies, the rest being unbarred. We reach a top precision of 94 per cent in identifying bars in galaxies using the trained network. This accuracy matches the accuracy reached by human experts on the same data without additional information about the images. Since deep convolutional neural networks can be scaled to handle large volumes of data, the method is expected to have great relevance in an era where astronomy data is rapidly increasing in terms of volume, variety, volatility, and velocity along with other V's that characterize big data. With the trained model, we have constructed a catalogue of barred galaxies from SDSS and made it available online.2018MNRAS.477..894Ahttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/477/894CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-tohttp://www.sdss.org : SDSS Home Pagehttps://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/MNRAS/477/894https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/MNRAS/477/894http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/MNRAS/477/894https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/MNRAS/477/894https://vizier.iucaa.in/viz-bin/votable?-source=J/MNRAS/477/894http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/MNRAS/477/894GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/MNRAS/477/894/table4 (Catalogue based on SDSS DR13)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/MNRAS/477/894/table4?https://vizier.iucaa.in/viz-bin/conesearch/J/MNRAS/477/894/table4?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/MNRAS/477/894/table4?GETtext/xml+votable180.050000true0.005815.50980.0055555555555555561/5 8 27 2/16-17 19 28 38 41-43 103 107 3/72-73 75 97 101-103 116 144 146-147 161-163 176-178 184 407 411 423 427 494 4/297 299 399 401-403 468-469 472 580 582-583 592 594 600 602 624 641-643 716 718 724 740 744-746 1375 1621-1623 1625-1627 1637-1639 1641-1643 1685-1687 1691 1703 1707 1961-1963 1965-1967 1980 1982-1983 2027 5/0 136 138 1184-1185 1187 1193 1540-1541 1549 1585 1588-1589 1601-1603 1685 1728-1729 1732 1880-1881 1884 1896-1898 1900-1901 2325 2327 2504-2506 2518 2521 2523-2524 2526 2528 2530 2536 2545 2547-2548 2550 2553 2555 2560 2562-2563 2868 2870 2876 2878 2968 2970 2988 3095 3098-3101 3137 4223 4307-4310 4312-4314 4325-4326 4328-4330 4351 4373-4375 4377-4380 4388-4390 4392-4394 4416 4437-4438 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31550-31551 31594 31598-31599 31611 31700 31704 31706-31707 31710 32391 32397 32399 32402-32403 32406 32422-32423 32425-32427 32460 32462 32478 32484 32486 32492 32494-32495 32497 32499 32505 32672 32674 32684 32686 36607 36703 36715 36721-36723 36759 36761-36763 36771 36773-36775 36788-36790 36792-36794 36812-36813 36816-36817 36832 36861-36863 40957 40959 45023 45039 45043 45045-45047 48983 49007 49009-49011 49020-49022 49077-49079 49081 49091 49108 49112 49124 49128 49130-49131 49149-49151https://cdsarc.cds.unistra.fr/viz-bin/moc/J/MNRAS/477/894?format=asciiOpticaldefaultJ/MNRAS/477/894/table4Catalogue based on SDSS DR13recnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintobjIDSDSS photometric object IDmeta.id;meta.mainRAJ2000Rigth ascension (J2000)degpos.eq.ra;meta.mainindexedDEJ2000Declination (J2000)degpos.eq.dec;meta.mainindexedClassMost probable class of the objectmeta.code.classcharProbPrediction probability for the most probable class%statdoubleSloanDisplay the SDSS data for this objectmeta.ref.urlchar