LEDA 1190386 , the SIMBAD biblio

LEDA 1190386 , the SIMBAD biblio (20 results) C.D.S. - SIMBAD4 rel 1.8 - 2026.10.15CEST00:10:09


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Title First 3 Authors
2003A&A...412...45P viz 15       D               1 956189 750 HYPERLEDA. I. Identification and designation of galaxies. PATUREL G., PETIT C., PRUGNIEL P., et al.
2013MNRAS.435.2903B 16       D               2 18 56 Galaxy And Mass Assembly: resolving the role of environment in galaxy evolution. BROUGH S., CROOM S., SHARP R., et al.
2014ApJ...788...45T viz 15       D               1 224166 54 Luminosity and redshift dependence of the covering factor of active galactic nuclei viewed with WISE and Sloan Digital Sky Survey. TOBA Y., OYABU S., MATSUHARA H., et al.
2015MNRAS.452.2087L viz 16       D               1 71490 449 Galaxy And Mass Assembly (GAMA): end of survey report and data release 2. LISKE J., BALDRY I.K., DRIVER S.P., et al.
2016A&A...590A..29P viz 16       D               1 13088 7 Multifrequency studies of galaxies and groups. I. Environmental effect on galaxy stellar mass and morphology. POUDEL A., HEINAMAKI P., NURMI P., et al.
2017A&A...599A..71D viz 16       D               1 209271 41 Aperture-free star formation rate of SDSS star-forming galaxies. DUARTE PUERTAS S., VILCHEZ J.M., IGLESIAS-PARAMO J., et al.
2018MNRAS.481.3497S viz 16       D               1 198 31 JINGLE, a JCMT legacy survey of dust and gas for galaxy evolution studies - I. Survey overview and first results. SAINTONGE A., WILSON C.D., XIAO T., et al.
2019MNRAS.486.4166S viz 16       D               1 199 3 JINGLE, a JCMT legacy survey of dust and gas for galaxy evolution studies: II. SCUBA-2 850 µm data reduction and dust flux density catalogues. SMITH M.W.L., CLARK C.J.R., DE LOOZE I., et al.
2019MNRAS.489.4389L viz 16       D               1 195 21 JINGLE - V. Dust properties of nearby galaxies derived from hierarchical Bayesian SED fitting. LAMPERTI I., SAINTONGE A., DE LOOZE I., et al.
2020ApJS..247...53K viz 16       D               1 23378 20 Radio sources associated with Optical Galaxies and having Unresolved or Extended morphologies (ROGUE). I. A catalog of SDSS galaxies with FIRST core identifications. KOZIEL-WIERZBOWSKA D., GOYAL A. and ZYWUCKA N.
2020MNRAS.496.3668D viz 16       D               1 421 31 JINGLE - IV. Dust, H I gas, and metal scaling laws in the local Universe. DE LOOZE I., LAMPERTI I., SAINTONGE A., et al.
2022MNRAS.509.3966W viz 17       D               1 89291 61 Galaxy Zoo DECaLS: Detailed visual morphology measurements from volunteers and deep learning for 314 000 galaxies. WALMSLEY M., LINTOTT C., GERON T., et al.
2022MNRAS.513..439D viz 17       D               1 302202 70 Galaxy And Mass Assembly (GAMA): Data Release 4 and the z < 0.1 total and z < 0.08 morphological galaxy stellar mass functions. DRIVER S.P., BELLSTEDT S., ROBOTHAM A.S.G., et al.
2022ApJS..262...36S viz 17       D               1 10075 23 SDSS-IV MaNGA: pyPipe3D Analysis Release for 10,000 Galaxies. SANCHEZ S.F., BARRERA-BALLESTEROS J.K., LACERDA E., et al.
2022A&A...666A.186D viz 17       D               1 194359 6 Mass-metallicity and star formation rate in galaxies: A complex relation tuned to stellar age. DUARTE PUERTAS S., VILCHEZ J.M., IGLESIAS-PARAMO J., et al.
2023A&A...674A..85A viz 17       D               1 6495 2 Classifying the full SDSS-IV MaNGA Survey using optical diagnostic diagrams: Presentation of AGN catalogs in flexible apertures. ALBAN M. and WYLEZALEK D.
2023ApJS..269....3M viz 17       D               1 356397 ~ Siena Galaxy Atlas 2020. MOUSTAKAS J., LANG D., DEY A., et al.
2024MNRAS.527.7438R viz 18       D               1 4215 ~ The Tully-Fisher relation from SDSS-MaNGA: physical causes of scatter and variation at different radii. RISTEA A., CORTESE L., FRASER-MCKELVIE A., et al.
2024ApJ...970...83B 18       D               1 2480 ~ Structure and Kinematics of Star-forming Elliptical Galaxies in SDSS-MaNGA. BISWAS P. and WADADEKAR Y.
2024NatAs...8.1310A viz 18       D               1 10075 ~ Constraints on the in situ and ex situ stellar masses in nearby galaxies obtained with artificial intelligence. ANGELOUDI E., FALCON-BARROSO J., HUERTAS-COMPANY M., et al.

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