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2025
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| Online Access: | https://doi.org/10.5281/zenodo.18026531 |
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| _version_ | 1866902336032473088 |
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| author | Kaushal, Neerav |
| author_facet | Kaushal, Neerav |
| contents | <p>This repository contains data and pretrained model checkpoint used in the nuGAN (neutrino GAN) paper.</p> <p>Data:<br>- scaled_density_contrast_maps.npy: 2D density maps of shape (15000,256,256). Maps correpsonding to neutrino masses 0.0, 0.1, 0.4, 0.8, and 12.2 can be chosen by indices [:3000], [3000:6000], [6000:9000], [9000,12000], and [12000:] respectively.<br>- neutrino_masses.npy: corresponding neutrino masses of shape (15000,). Same indexing for as above.</p> <p>Model:<br>- Trained nuGAN checkpoint state dict</p> <p> </p> <p>These resources are provided without restrictions.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18026531 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | νGAN: A Deep Learning Emulator for Cosmic Web Simulations with Massive Neutrinos Kaushal, Neerav <p>This repository contains data and pretrained model checkpoint used in the nuGAN (neutrino GAN) paper.</p> <p>Data:<br>- scaled_density_contrast_maps.npy: 2D density maps of shape (15000,256,256). Maps correpsonding to neutrino masses 0.0, 0.1, 0.4, 0.8, and 12.2 can be chosen by indices [:3000], [3000:6000], [6000:9000], [9000,12000], and [12000:] respectively.<br>- neutrino_masses.npy: corresponding neutrino masses of shape (15000,). Same indexing for as above.</p> <p>Model:<br>- Trained nuGAN checkpoint state dict</p> <p> </p> <p>These resources are provided without restrictions.</p> |
| title | νGAN: A Deep Learning Emulator for Cosmic Web Simulations with Massive Neutrinos |
| url | https://doi.org/10.5281/zenodo.18026531 |