GOODS-ALMA: 1.1 mm galaxy survey - I. Source catalogue and optically dark galaxies
M. Franco, D. Elbaz, M. B\'ethermin, B. Magnelli, C. Schreiber, L., Ciesla, M. Dickinson, N. Nagar, J. Silverman, E. Daddi, D. M. Alexander, T., Wang, M. Pannella, E. Le Floc'h, A. Pope, M. Giavalisco, A. J. Maury, F., Bournaud, R. Chary, R. Demarco, H. Ferguson

TL;DR
This paper presents a deep 1.1mm ALMA survey of the GOODS-South field, identifying 20 robust galaxy detections, including optically dark galaxies, and analyzing their properties and implications for galaxy evolution.
Contribution
It provides the first extensive ALMA-based galaxy catalogue in GOODS-South, including optically dark galaxies and insights into their redshifts, masses, and AGN activity, advancing understanding of high-redshift galaxy populations.
Findings
Detected 20 galaxies with >80% purity, including optically dark ones.
Median redshift of detected galaxies is 2.92, with some at z>4.
Approximately 40% host X-ray AGN, higher than similar-mass galaxies.
Abstract
We present a 69 arcmin ALMA survey at 1.1mm, GOODS-ALMA, matching the deepest HST-WFC3 H-band part of the GOODS-South field. We taper the 0"24 original image with a homogeneous and circular synthesized beam of 0"60 to reduce the number of independent beams - thus reducing the number of purely statistical spurious detections - and optimize the sensitivity to point sources. We extract a catalogue of galaxies purely selected by ALMA and identify sources with and without HST counterparts down to a 5 limiting depth of H=28.2 AB (HST/WFC3 F160W). ALMA detects 20 sources brighter than 0.7 mJy in the 0"60 tapered mosaic (rms sensitivity =0.18 mJy/beam) with a purity greater than 80%. Among these detections, we identify three sources with no HST nor Spitzer-IRAC counterpart, consistent with the expected number of spurious galaxies from the analysis of the inverted image; their…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research · Adaptive optics and wavefront sensing
