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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.22311 |
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| _version_ | 1866912928345620480 |
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| author | Pearson, James Dickinson, Hugh Serjeant, Stephen Walmsley, Mike Fortson, Lucy Kruk, Sandor Masters, Karen L. Simmons, Brooke D. Smethurst, R. J. Lintott, Chris Zalesky, Lukas McPartland, Conor Weaver, John R. Toft, Sune Sanders, Dave Chartab, Nima McCracken, Henry Joy Mobasher, Bahram Szapudi, Istvan East, Noah Turner, Wynne Malkan, Matthew Pearson, William J. Goto, Tomotsugu Oi, Nagisa |
| author_facet | Pearson, James Dickinson, Hugh Serjeant, Stephen Walmsley, Mike Fortson, Lucy Kruk, Sandor Masters, Karen L. Simmons, Brooke D. Smethurst, R. J. Lintott, Chris Zalesky, Lukas McPartland, Conor Weaver, John R. Toft, Sune Sanders, Dave Chartab, Nima McCracken, Henry Joy Mobasher, Bahram Szapudi, Istvan East, Noah Turner, Wynne Malkan, Matthew Pearson, William J. Goto, Tomotsugu Oi, Nagisa |
| contents | We present morphological classifications of over 41,000 galaxies out to $z_{\rm phot}\sim2.5$ 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 $m_{HSC-i} = 21.5$. 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 $z_{\rm phot}$ of $0.42\pm0.23$), 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22311 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 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 Dickinson, Hugh Serjeant, Stephen Walmsley, Mike Fortson, Lucy Kruk, Sandor Masters, Karen L. Simmons, Brooke D. Smethurst, R. J. Lintott, Chris Zalesky, Lukas McPartland, Conor Weaver, John R. Toft, Sune Sanders, Dave Chartab, Nima McCracken, Henry Joy Mobasher, Bahram Szapudi, Istvan East, Noah Turner, Wynne Malkan, Matthew Pearson, William J. Goto, Tomotsugu Oi, Nagisa Astrophysics of Galaxies Instrumentation and Methods for Astrophysics We present morphological classifications of over 41,000 galaxies out to $z_{\rm phot}\sim2.5$ 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 $m_{HSC-i} = 21.5$. 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 $z_{\rm phot}$ of $0.42\pm0.23$), 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. |
| title | 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 |
| topic | Astrophysics of Galaxies Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2509.22311 |