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ALMA Deep Field in the SSA22 proto-cluster at z = 3

Published online by Cambridge University Press:  04 June 2020

Hideki Umehata*
Affiliation:
RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan email: hideki.umehata@riken.jp Institute of Astronomy, School of Science, The University of Tokyo, 2-21-1 Osawa, Mitaka, Tokyo 181-0015, Japan
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Abstract

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Galaxies and nuclei in dense environment at high redshift provide a good laboratory to investigate accelerated, most extreme evolution of galaxies. The SSA22 proto-cluster at z = 3.1 is known to have a three-dimensional 50 (comoving) Mpc-scale filamentary structure, traced by Lyα emitters, which makes the field a suitable target in this regard. To identify dust-obscured star-formation, a contiguous 20 arcmin2 region at the node of the cosmic structure was observed in ALMA band 6. In total 57 ALMA sources have been identified above 5σ, which makes the field one of the richest field in ALMA-identified (sub)millimeter galaxies. The follow-up spectroscopy confirmed about 20 sources as exact proto-cluster members so far. Together with high X-ray AGN fraction, our results suggest that the vigorous star formation activity and the growth of super massive black holes occurred simultaneously in the densest regions at z ∼ 3.

Type
Contributed Papers
Copyright
© International Astronomical Union 2020

References

Blain, A. W.et al. 2002, PhR, 369, 111Google Scholar
Bond, J. R.et al. 1996, MNRAS, 380, 603Google Scholar
Bothwell, M. S.et al. 2013, MNRAS, 429, 304710.1093/mnras/sts562CrossRefGoogle Scholar
Casey, C. M.et al. 2014, PhR, 541, 45Google Scholar
Dekel, A.et al. 2009, Nature, 457, 45110.1038/nature07648CrossRefGoogle Scholar
De Lucia, G.et al. 2006, MNRAS, 366, 49910.1111/j.1365-2966.2005.09879.xCrossRefGoogle Scholar
Dressler, A. 1980, ApJ, 236, 35110.1086/157753CrossRefGoogle Scholar
Dunlop, J. S.et al. 2017, MNRAS, 466, 86110.1093/mnras/stw3088CrossRefGoogle Scholar
Franco, M.et al. 2018, A&A, 620, 152Google Scholar
Gruppioni, C.et al. 2013, MNRAS, 432, 2310.1093/mnras/stt308CrossRefGoogle Scholar
Hatsukade, B.et al. 2018, PASJ, 70, 10510.1093/pasj/psy104CrossRefGoogle Scholar
Iono, D.et al. 2009, ApJ, 695, 153710.1088/0004-637X/695/2/1537CrossRefGoogle Scholar
Kauffmann, G.et al. 1999, MNRAS, 307, 52910.1046/j.1365-8711.1999.02711.xCrossRefGoogle Scholar
Lehmer, B. D.et al. 2009, ApJ., 691, 68710.1088/0004-637X/691/1/687CrossRefGoogle Scholar
Lehmer, B. D.et al. 2009, MNRAS., 400, 299CrossRefGoogle Scholar
Matsuda, Y.et al. 2005, ApJ, 634, 12510.1086/499071CrossRefGoogle Scholar
Overzier, R. A.et al. 2016, A&ARv, 24, 14Google Scholar
Steidel, C. C.et al. 1998, ApJ, 492, 428CrossRefGoogle Scholar
Tamura, Y.et al. 2009, Nature, 459, 6110.1038/nature07947CrossRefGoogle Scholar
Tamura, Y.et al. 2010, ApJ, 724, 127010.1088/0004-637X/724/2/1270CrossRefGoogle Scholar
Umehata, H.et al. 2014, MNRAS, 440, 346210.1093/mnras/stu447CrossRefGoogle Scholar
Umehata, H.et al. 2015, ApJ, 815, L810.1088/2041-8205/815/1/L8CrossRefGoogle Scholar
Umehata, H.et al. 2017, ApJ, 835, 9810.3847/1538-4357/835/1/98CrossRefGoogle Scholar
Umehata, H.et al. 2018, PASJ, 70, 6510.1093/pasj/psy065CrossRefGoogle Scholar
Umehata, H.et al. 2019, submitted to ScienceGoogle Scholar
Venemans, B. P.et al. 2002, ApJ, 569, 1110.1086/340563CrossRefGoogle Scholar
Wardlow, J. L.et al. 2018, MNRAS, 479, 387910.1093/mnras/sty1526CrossRefGoogle Scholar
Yamada, T.et al. 2012, AJ, 143, 7910.1088/0004-6256/143/4/79CrossRefGoogle Scholar