Learning Cosmology from Nearest Neighbour Statistics
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914161279107072 |
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| author | Chatterjee, Atrideb Banerjee, Arka Villaescusa-Navarro, Francisco Abel, Tom |
| author_facet | Chatterjee, Atrideb Banerjee, Arka Villaescusa-Navarro, Francisco Abel, Tom |
| contents | Extracting cosmological parameters from galaxy/halo catalogues with sub-percent level accuracy is an important aspect of modern cosmology, especially in view of ongoing and upcoming surveys such as Euclid, DESI, and LSST. While traditional two-point statistics have been known to be suboptimal for this task, recently proposed k-Nearest Neighbour (kNN) based summary statistics have demonstrated tighter constraining power. Building on the kNN statistics, we introduce a new field-level representation of discrete halo catalogues - NN distance maps. We employ this technique on the halo catalogues obtained from Quijote N-body simulation suites. By combining these maps with kNN-based summary statistics, we train a hybrid neural network to infer cosmological parameters, showing that the resulting constraints achieve state-of-the-art, if not the best, accuracy. In addition, our hybrid framework is 5-10 times more computationally efficient than some of the existing point-cloud-based ML methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13393 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Cosmology from Nearest Neighbour Statistics Chatterjee, Atrideb Banerjee, Arka Villaescusa-Navarro, Francisco Abel, Tom Cosmology and Nongalactic Astrophysics Extracting cosmological parameters from galaxy/halo catalogues with sub-percent level accuracy is an important aspect of modern cosmology, especially in view of ongoing and upcoming surveys such as Euclid, DESI, and LSST. While traditional two-point statistics have been known to be suboptimal for this task, recently proposed k-Nearest Neighbour (kNN) based summary statistics have demonstrated tighter constraining power. Building on the kNN statistics, we introduce a new field-level representation of discrete halo catalogues - NN distance maps. We employ this technique on the halo catalogues obtained from Quijote N-body simulation suites. By combining these maps with kNN-based summary statistics, we train a hybrid neural network to infer cosmological parameters, showing that the resulting constraints achieve state-of-the-art, if not the best, accuracy. In addition, our hybrid framework is 5-10 times more computationally efficient than some of the existing point-cloud-based ML methods. |
| title | Learning Cosmology from Nearest Neighbour Statistics |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2511.13393 |