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ニシミチ タカヒロ
NISHIMICHI TAKAHIRO
西道 啓博 所属 京都産業大学 理学部 宇宙物理・気象学科 職種 准教授 |
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| 言語種別 | 英語 |
| 発行・発表の年月 | 2025/09 |
| 形態種別 | 研究論文 |
| 査読 | 査読あり |
| 標題 | A Parameter-masked Mock Data Challenge for Beyond-two-point Galaxy Clustering Statistics |
| 執筆形態 | 共著 |
| 掲載誌名 | The Astrophysical Journal |
| 掲載区分 | 国外 |
| 出版社・発行元 | American Astronomical Society |
| 巻・号・頁 | 990(2),pp.id.99 |
| 国際共著 | 国際共著 |
| 著者・共著者 | Elisabeth Krause,Yosuke Kobayashi,Andrés N. Salcedo,Mikhail M. Ivanov,Tom Abel,Kazuyuki Akitsu,Raul E. Angulo,Giovanni Cabass,Sofia Contarini,Carolina Cuesta-Lazaro,ChangHoon Hahn,Nico Hamaus,Donghui Jeong,Chirag Modi,Nhat-Minh Nguyen,Takahiro Nishimichi,Enrique Paillas,Marcos Pellejero Ibañez,Oliver H. E. Philcox,Alice Pisani,Fabian Schmidt,Satoshi Tanaka,Giovanni Verza,Sihan Yuan,Matteo Zennaro |
| 概要 | Abstract
The past 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 named “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 data set consists of high-precision mock galaxy catalogs for clustering in real space, in 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 ΛCDM 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. |
| DOI | 10.3847/1538-4357/ad781d |
| ISSN | 0004-637X/1538-4357 |
| PermalinkURL | https://iopscience.iop.org/article/10.3847/1538-4357/ad781d |
| researchmap用URL | https://iopscience.iop.org/article/10.3847/1538-4357/ad781d/pdf |