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| Main Authors: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en liña: | https://arxiv.org/abs/2605.20027 |
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| _version_ | 1866910236760080384 |
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| author | Voskresenskaia, S. Lyskova, N. Zaznobin, I. Meshcheryakov, A. |
| author_facet | Voskresenskaia, S. Lyskova, N. Zaznobin, I. Meshcheryakov, A. |
| contents | Machine-learning methods are increasingly applied to astronomical surveys, providing powerful tools for detecting and studying galaxy clusters. We investigate the mass-redshift properties and completeness of the ComPACT galaxy cluster catalogue, constructed using a convolutional neural network applied to publicly available combined ACT+Planck maps. The ComPACT catalogue contains 2,962 SZ-selected galaxy cluster candidates. We confirm clusters by estimating redshifts using literature information and photometric techniques based on DESI Legacy Imaging Surveys data. Cluster masses are derived from ACT+Planck and Planck Compton-y maps via SZ scaling relations. The completeness is assessed using simulated cluster injections into real microwave maps. We confirm approximately $\sim$60 % of the ComPACT candidates as galaxy clusters. The redshifts span the range $0.007 < z < 1.7$, including approximately 116 new measurements. Masses are obtained for 56 % of the sample, covering the range $(0.25 - 13.1) \times 10^{14} ~M_\odot$ and including 158 new mass determinations. We identify five previously unreported massive clusters ($M_{500c} > 6 \times 10^{14}~M_\odot$) at $z > 0.7$, increasing the known population of such systems by approximately 10 %. The ComPACT catalogue expands the SZ-selected Planck-like cluster population, especially at high redshift and high mass, demonstrating the effectiveness of deep-learning approaches for cluster detection in microwave data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_20027 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ComPACT: Mass-Redshift Properties of the galaxy cluster catalogue Voskresenskaia, S. Lyskova, N. Zaznobin, I. Meshcheryakov, A. Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Machine-learning methods are increasingly applied to astronomical surveys, providing powerful tools for detecting and studying galaxy clusters. We investigate the mass-redshift properties and completeness of the ComPACT galaxy cluster catalogue, constructed using a convolutional neural network applied to publicly available combined ACT+Planck maps. The ComPACT catalogue contains 2,962 SZ-selected galaxy cluster candidates. We confirm clusters by estimating redshifts using literature information and photometric techniques based on DESI Legacy Imaging Surveys data. Cluster masses are derived from ACT+Planck and Planck Compton-y maps via SZ scaling relations. The completeness is assessed using simulated cluster injections into real microwave maps. We confirm approximately $\sim$60 % of the ComPACT candidates as galaxy clusters. The redshifts span the range $0.007 < z < 1.7$, including approximately 116 new measurements. Masses are obtained for 56 % of the sample, covering the range $(0.25 - 13.1) \times 10^{14} ~M_\odot$ and including 158 new mass determinations. We identify five previously unreported massive clusters ($M_{500c} > 6 \times 10^{14}~M_\odot$) at $z > 0.7$, increasing the known population of such systems by approximately 10 %. The ComPACT catalogue expands the SZ-selected Planck-like cluster population, especially at high redshift and high mass, demonstrating the effectiveness of deep-learning approaches for cluster detection in microwave data. |
| title | ComPACT: Mass-Redshift Properties of the galaxy cluster catalogue |
| topic | Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2605.20027 |