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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.02252 |
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| _version_ | 1866916234571808768 |
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| author | 2pt Collaboration Krause, Elisabeth Kobayashi, Yosuke Salcedo, Andrés N. Ivanov, Mikhail M. Abel, Tom Akitsu, Kazuyuki Angulo, Raul E. Cabass, Giovanni Contarini, Sofia Cuesta-Lazaro, Carolina Hahn, ChangHoon Hamaus, Nico Jeong, Donghui Modi, Chirag Nguyen, Nhat-Minh Nishimichi, Takahiro Paillas, Enrique Ibañez, Marcos Pellejero Philcox, Oliver H. E. Pisani, Alice Schmidt, Fabian Tanaka, Satoshi Verza, Giovanni Yuan, Sihan Zennaro, Matteo |
| author_facet | 2pt Collaboration Krause, Elisabeth Kobayashi, Yosuke Salcedo, Andrés N. Ivanov, Mikhail M. Abel, Tom Akitsu, Kazuyuki Angulo, Raul E. Cabass, Giovanni Contarini, Sofia Cuesta-Lazaro, Carolina Hahn, ChangHoon Hamaus, Nico Jeong, Donghui Modi, Chirag Nguyen, Nhat-Minh Nishimichi, Takahiro Paillas, Enrique Ibañez, Marcos Pellejero Philcox, Oliver H. E. Pisani, Alice Schmidt, Fabian Tanaka, Satoshi Verza, Giovanni Yuan, Sihan Zennaro, Matteo |
| contents | The last few years have seen the emergence of a wide array of novel techniques for analyzing high-precision data from upcoming galaxy surveys, which aim to extend the statistical analysis of galaxy clustering data beyond the linear regime and the canonical two-point (2pt) statistics. We test and benchmark some of these new techniques in a community data challenge "Beyond-2pt", initiated during the Aspen 2022 Summer Program "Large-Scale Structure Cosmology beyond 2-Point Statistics," whose first round of results we present here. The challenge dataset consists of high-precision mock galaxy catalogs for clustering in real space, redshift space, and on a light cone. Participants in the challenge have developed end-to-end pipelines to analyze mock catalogs and extract unknown ("masked") cosmological parameters of the underlying $Λ$CDM models with their methods. The methods represented are density-split clustering, nearest neighbor statistics, BACCO power spectrum emulator, void statistics, LEFTfield field-level inference using effective field theory (EFT), and joint power spectrum and bispectrum analyses using both EFT and simulation-based inference. In this work, we review the results of the challenge, focusing on problems solved, lessons learned, and future research needed to perfect the emerging beyond-2pt approaches. The unbiased parameter recovery demonstrated in this challenge by multiple statistics and the associated modeling and inference frameworks supports the credibility of cosmology constraints from these methods. The challenge data set is publicly available and we welcome future submissions from methods that are not yet represented. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02252 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Parameter-Masked Mock Data Challenge for Beyond-Two-Point Galaxy Clustering Statistics 2pt Collaboration Krause, Elisabeth Kobayashi, Yosuke Salcedo, Andrés N. Ivanov, Mikhail M. Abel, Tom Akitsu, Kazuyuki Angulo, Raul E. Cabass, Giovanni Contarini, Sofia Cuesta-Lazaro, Carolina Hahn, ChangHoon Hamaus, Nico Jeong, Donghui Modi, Chirag Nguyen, Nhat-Minh Nishimichi, Takahiro Paillas, Enrique Ibañez, Marcos Pellejero Philcox, Oliver H. E. Pisani, Alice Schmidt, Fabian Tanaka, Satoshi Verza, Giovanni Yuan, Sihan Zennaro, Matteo Cosmology and Nongalactic Astrophysics The last few years have seen the emergence of a wide array of novel techniques for analyzing high-precision data from upcoming galaxy surveys, which aim to extend the statistical analysis of galaxy clustering data beyond the linear regime and the canonical two-point (2pt) statistics. We test and benchmark some of these new techniques in a community data challenge "Beyond-2pt", initiated during the Aspen 2022 Summer Program "Large-Scale Structure Cosmology beyond 2-Point Statistics," whose first round of results we present here. The challenge dataset consists of high-precision mock galaxy catalogs for clustering in real space, redshift space, and on a light cone. Participants in the challenge have developed end-to-end pipelines to analyze mock catalogs and extract unknown ("masked") cosmological parameters of the underlying $Λ$CDM models with their methods. The methods represented are density-split clustering, nearest neighbor statistics, BACCO power spectrum emulator, void statistics, LEFTfield field-level inference using effective field theory (EFT), and joint power spectrum and bispectrum analyses using both EFT and simulation-based inference. In this work, we review the results of the challenge, focusing on problems solved, lessons learned, and future research needed to perfect the emerging beyond-2pt approaches. The unbiased parameter recovery demonstrated in this challenge by multiple statistics and the associated modeling and inference frameworks supports the credibility of cosmology constraints from these methods. The challenge data set is publicly available and we welcome future submissions from methods that are not yet represented. |
| title | A Parameter-Masked Mock Data Challenge for Beyond-Two-Point Galaxy Clustering Statistics |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2405.02252 |