ECOGAL I. Project design and the first catalogue

TL;DR

ECOGAL is a comprehensive ALMA data mining project that creates extensive source catalogues in key galaxy fields, enabling new studies of galaxy evolution, gas, and dust content across cosmic time.

Contribution

It introduces a uniform reprocessing of ALMA data combined with JWST and HST footprints, expanding previous surveys and providing publicly accessible, science-ready catalogues for the community.

Findings

1288 detections with optical/NIR counterparts identified

Expansion of parameter space with new UDS field and JWST data

Enables studies of cosmic gas, dust, and obscured star formation

Abstract

We present ECOology for Galaxies using ALMA archive and Legacy surveys (ECOGAL), an ALMA data mining project. Using the footprints of the James Webb Space Telescope (JWST) and the Hubble Space Telescope (HST), we query and uniformly reprocess ALMA data to produce continuum images and two complementary source catalogues: (i) a prior-based catalogue anchored to optical/near-infrared detections, and (ii) a blind catalogue based on significant ALMA detections. Detection thresholds are established through peak SNR statistics and analysis of inverted maps. In this paper, we focus on the ALMA-accessible CANDELS fields (COSMOS, GOODS-S, and UDS), covering ~130,000 optical/NIR-selected galaxies spanning $0.0 < z < 15.0$. We identify 1288 detections (622 unique) with optical/NIR counterparts across the two methods, of which 832 detections (395 unique) appear in both. Among the 622 unique sources, 307 have spectroscopic redshifts from $0.12 < z < 6.85$. ECOGAL expands the parameter space previously explored by $A^3COSMOS$ and $A^3GOODS-S$ by incorporating the UDS field and integrating publicly available JWST datasets from the DAWN JWST Archive (DJA). We also highlight several science cases enabled by ECOGAL, including the evolution of cosmic gas and dust masses, obscured star formation and optically dark systems. ECOGAL provides science-ready data sets for the community and showcases the power of combining over a decade of accumulated public data with other legacy datasets. The data product is made publicly available and accompanies a post demonstrating the usage of the catalogue on the DJA webpage.