The ALMA Frontier Fields Survey III: 1.1 mm Emission Line Identifications in Abell 2744, MACSJ0416.1-2403, MACSJ1149.5+2223, Abell 370, and Abell S1063 release_qu3smqq7ojc4xna5gyg6qs766m

Abstract

Most sub-mm emission line studies of galaxies to date have targeted sources with known redshifts where the frequencies of the lines are well constrained. Recent blind line scans circumvent the spectroscopic redshift requirement, which could represent a selection bias. Our aim is to detect emission lines present in continuum oriented observations. The detection of such lines provides spectroscopic redshift and yields properties of the galaxies. We perform a search for emission lines in the ALMA observations of five Frontier Fields clusters and assess the reliability of our detection by associating line candidates with detected galaxies in deep near-infrared imaging. We find 26 significant emission lines candidates, with observed line fluxes between 0.2-4.6 Jy km s^-1 and velocity dispersions (FWHM) of 25-600 km s^-1. Nine of these candidates lie nearby to near-infrared sources, boosting their reliability; in six cases the observed line frequency and strength are consistent with expectations given the photometric redshift and properties of the galaxy counterparts. We present redshift identifications, magnifications and molecular gas estimates for the galaxies with identified lines. We show that two of these candidates likely originate from starburst galaxies, one of which is a jellyfish galaxy, while another two are consistent with being main sequence galaxies based in their depletion times. This work highlights the degree to which serendipitous emission lines can be discovered in large mosaic continuum observations when deep ancillary data are available. The low number of high-significance line detections, however, confirms that such surveys are not as optimal as blind line scans. We stress that Monte Carlo simulations should be used to assess the line detections significances, since using the negative noise suffers from stochasticity and incurs larger uncertainties.
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