Radio galaxy zoo data release 1: 100,185 radio source classifications from the FIRST and ATLAS surveys

Wong, O Ivy and Garon, A F and Alger, M J and Rudnick, L and Shabala, S S and Willett, K W and Banfield, J K and Andernach, H and Norris, R P and Swan, J and Hardcastle, M J and Lintott, C J and White, S V and Seymour, N and Kapińska, A D and Tang, H and Simmons, B D and Schawinski, K (2025) Radio galaxy zoo data release 1: 100,185 radio source classifications from the FIRST and ATLAS surveys. Monthly Notices of the Royal Astronomical Society, 536 (4). pp. 3488-3506. ISSN 0035-8711

Abstract

Radio galaxies can extend far beyond the stellar component of their originating host galaxies, and their radio emission can consist of multiple discrete components. Furthermore, the apparent source structure will depend on survey sensitivity, resolution and the observing frequency. Associated discrete radio components and their originating host galaxy are typically identified through a visual comparison of radio and mid-infrared survey images. We present the first data release of Radio Galaxy Zoo, an online citizen science project that enlists the help of citizen scientists to cross-match extended radio sources from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) and the Australia Telescope Large Area Survey (ATLAS) surveys, often with complex structure, to host galaxies in 3.6 μm infrared images from the Wide-field Infrared Survey Explorer (WISE) and the Spitzer Space Telescope. This first data release consists of 100,185 classifications for 99,146 radio sources from the FIRST survey and 583 radio sources from the ATLAS survey. We include two tables for each of the FIRST and ATLAS surveys: 1) the identification of all components making up each radio source; and 2) the cross-matched host galaxies. These classifications have an average reliability of 0.83 based on the weighted consensus levels of our citizen scientists. The reliability of the DR1 catalogue has been further demonstrated through several parallel studies which used the pre-release versions of this catalogue to train and prototype machine learning-based classifiers. We also include a brief description of the radio source populations catalogued by RGZ DR1.