Galaxy Zoo: Cosmic Dawn – morphological classifications for over 41 000 galaxies in the Euclid Deep Field North from the Hawaii Two-0 Cosmic Dawn survey

Pearson, James and Dickinson, Hugh and Serjeant, Stephen and Walmsley, Mike and Fortson, Lucy and Kruk, Sandor and Masters, Karen L and Simmons, Brooke D and Smethurst, R J and Lintott, Chris and Zalesky, Lukas and McPartland, Conor and Weaver, John R and Toft, Sune and Sanders, Dave and Chartab, Nima and McCracken, Henry Joy and Mobasher, Bahram and Szapudi, Istvan and East, Noah and Turner, Wynne and Malkan, Matthew and Pearson, William J and Goto, Tomotsugu and Oi, Nagisa (2025) Galaxy Zoo: Cosmic Dawn – morphological classifications for over 41 000 galaxies in the Euclid Deep Field North from the Hawaii Two-0 Cosmic Dawn survey. Monthly Notices of the Royal Astronomical Society. ISSN 0035-8711

Abstract

We present morphological classifications of over 41 000 galaxies out to zphot ∼ 2.5 across six square degrees of the Euclid Deep Field North (EDFN) from the Hawaii Twenty Square Degree (H20) survey, a part of the wider Cosmic Dawn survey. Galaxy Zoo citizen scientists play a crucial role in the examination of large astronomical data sets through crowdsourced data mining of extragalactic imaging. This iteration, Galaxy Zoo: Cosmic Dawn (GZCD), saw tens of thousands of volunteers and the deep learning foundation model Zoobot collectively classify objects in ultra-deep multiband Hyper Suprime-Cam (HSC) imaging down to a depth of mHSC − i = 21.5. Here, we present the details and general analysis of this iteration, including the use of Zoobot in an active learning cycle to improve both model performance and volunteer experience, as well as the discovery of 51 new gravitational lenses in the EDFN. We also announce the public data release of the classifications for over 45 000 subjects, including more than 41 000 galaxies (median zphot of 0.42 ± 0.23), along with their associated image cutouts. This data set provides a valuable opportunity for follow-up imaging of objects in the EDFN as well as acting as a truth set for training deep learning models for application to ground-based surveys like that of the Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) collaboration and the newly operational Vera C. Rubin Observatory.