Automated Mining of the ALMA Archive in the COSMOS Field (A(3)COSMOS). I. Robust ALMA Continuum Photometry Catalogs and Stellar Mass and Star Formation Properties for similar to 700 Galaxies at z=0.5-6

Abstract

The rich information on (sub)millimeter dust continuum emission from distant galaxies in the public Atacama Large Millimeter/submillimeter Array (ALMA) archive is contained in thousands of inhomogeneous observations from individual PI-led programs. To increase the usability of these data for studies deepening our understanding of galaxy evolution, we have developed automated mining pipelines for the ALMA archive in the COSMOS field (A3COSMOS) that efficiently exploit the available information for large numbers of galaxies across cosmic time and keep the data products in sync with the increasing public ALMA archive: (a) a dedicated ALMA continuum imaging pipeline, (b) two complementary photometry pipelines for both blind source extraction and prior source fitting, (c) a counterpart association pipeline utilizing the multiwavelength data available (including quality assessment based on machine-learning techniques), (d) an assessment of potential (sub)millimeter line contribution to the measured ALMA continuum, and (e) extensive simulations to provide statistical corrections to biases and uncertainties in the ALMA continuum measurements. Application of these tools yields photometry catalogs with ~1000 (sub)millimeter detections (spurious fraction ~8%-12%) from over 1500 individual ALMA continuum images. Combined with ancillary photometric and redshift catalogs and the above quality assessments, we provide robust information on redshift, stellar mass, and star formation rate for ~700 galaxies at redshifts 0.5-6 in the COSMOS field (with undetermined selection function). The ALMA photometric measurements and galaxy properties are released publicly within our blind extraction, prior fitting, and galaxy property catalogs, plus the images. These products will be updated on a regular basis in the future. ; D.L., P.L., and E.S. acknowledge support and funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 694343). S.L. acknowledges ...