MUSE-WIDE 123005089 , the SIMBAD biblio

MUSE-WIDE 123005089 , the SIMBAD biblio (21 results) C.D.S. - SIMBAD4 rel 1.8 - 2026.10.15CEST14:21:53

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Bibcode/DOI Score in Title|Abstract|
Keywords 		in a table in teXt, Caption, ... Nb occurence Nb objects in ref Citations
(from ADS) 		Title First 3 Authors
2011ApJS..195...10X viz 15       D               1 782 508 The Chandra Deep Field-South survey: 4 Ms source catalogs. XUE Y.Q., LUO B., BRANDT W.N., et al.
2014ApJ...796...60H viz 15       D               1 1489 120 CANDELS/GOODS-S, CDFS, and ECDFS : photometric redshifts for normal and X-Ray-detected galaxies. HSU L.-T., SALVATO M., NANDRA K., et al.
2016MNRAS.457..629C viz 16       D               1 650 33 ZFOURGE catalogue of AGN candidates: an enhancement of 160-µm-derived star formation rates in active galaxies to z = 3.2. COWLEY M.J., SPITLER L.R., TRAN K.-V.H., et al.
2017ApJS..228....2L viz 16       D               1 1061 334 The Chandra Deep Field-South survey: 7 Ms source catalogs. LUO B., BRANDT W.N., XUE Y.Q., et al.
2017A&A...606A..12H viz 16       D               1 832 83 The MUSE-Wide survey: A first catalogue of 831 emission line galaxies. HERENZ E.C., URRUTIA T., WISOTZKI L., et al.
2018MNRAS.475.5133R viz 16       D               1 11242 2 Testing the hierarchical assembly of massive galaxies using accurate merger rates out to z ∼ 1.5. RODRIGUES M., PUECH M., FLORES H., et al.
2018MNRAS.473.2378V 16       D               1 123 109 High-redshift agn in the Chandra Deep Fields: the obscured fraction and space density of the sub-L* population. VITO F., BRANDT W.N., YANG G., et al.
2018ApJS..237...12O viz 16       D               1 25243 42 HDUV: the Hubble Deep UV Legacy Survey. OESCH P.A., MONTES M., REDDY N., et al.
2018ApJ...865..106C viz 16       D               1 93 54 A submillimeter perspective on the GOODS fields (SUPER GOODS). III. A large sample of ALMA sources in the GOODS-S. COWIE L.L., GONZALEZ-LOPEZ J., BARGER A.J., et al.
2018A&A...620A.152F 20       D               3 31 153 GOODS-ALMA: 1.1 mm galaxy survey. I. Source catalog and optically dark galaxies. FRANCO M., ELBAZ D., BETHERMIN M., et al.
2019A&A...624A.141U viz 16       D               3 1906 76 The MUSE-Wide Survey: survey description and first data release. URRUTIA T., WISOTZKI L., KERUTT J., et al.
2019ApJ...887...23B viz 16       D               1 942 ~ A submillimeter perspective on the GOODS fields (SUPER GOODS). IV. The submillimeter properties of X-ray sources in the CDF-S. BARGER A.J., COWIE L.L., BAUER F.E., et al.
2020MNRAS.492.1887G viz 16       D               1 792 ~ Physical properties of the CDFS X-ray sources through fitting spectral energy distributions. GUO X., GU Q., DING N., et al.
2020A&A...642A.155Z 3294     A D S   X C       79 19 21 GOODS-ALMA: Optically dark ALMA galaxies shed light on a cluster in formation at z = 3.5. ZHOU L., ELBAZ D., FRANCO M., et al.
2020A&A...643A..30F 17       D               2 38 38 GOODS-ALMA: The slow downfall of star formation in z = 2-3 massive galaxies. FRANCO M., ELBAZ D., ZHOU L., et al.
2021ApJ...908..169G 476       D     X         12 53 ~ Multiwavelength selected Compton-thick AGNs in Chandra Deep Field-South survey. GUO X., GU Q., DING N., et al.
2022A&A...658A..43G 17       D               1 54 37 GOODS-ALMA 2.0: Source catalog, number counts, and prevailing compact sizes in 1.1 mm galaxies. GOMEZ-GUIJARRO C., ELBAZ D., XIAO M., et al.
2023ApJ...952...28C 17       D               1 77 1 2 mm Observations and the Search for High-redshift Dusty Star-forming Galaxies. COWIE L.L., BARGER A.J. and BAUER F.E.
2024A&A...689A.327W viz 18       D               1 211 ~ The fundamental plane of black hole activity for low-luminosity radio active galactic nuclei across 0 < z < 4. WANG Y., WANG T., HO L.C., et al.
2025ApJ...980...13T 19       D               1 82 ~ Rare Occasions: Tidal Disruption Events Rarely Power the AGNs Observed in Dwarf Galaxies. TAN J., YANG G., WALSH J.L., et al.
2025ApJ...987...46Z 19       D               1 466 ~ Identifying Compton-thick Active Galactic Nuclei with a Machine Learning Algorithm in Chandra Deep Field-South. ZHANG R., GUO X., GU Q., et al.

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