Constraining νΛCDM with density-split clustering

التفاصيل البيبلوغرافية
العنوان: Constraining νΛCDM with density-split clustering
المؤلفون: Enrique Paillas, Carolina Cuesta-Lazaro, Pauline Zarrouk, Yan-Chuan Cai, Will J Percival, Seshadri Nadathur, Mathilde Pinon, Arnaud de Mattia, Florian Beutler
بيانات النشر: arXiv, 2022.
سنة النشر: 2022
مصطلحات موضوعية: Cosmology and Nongalactic Astrophysics (astro-ph.CO), Space and Planetary Science, FOS: Physical sciences, Astronomy and Astrophysics
الوصف: The dependence of galaxy clustering on local density provides an effective method for extracting non-Gaussian information from galaxy surveys. The two-point correlation function (2PCF) provides a complete statistical description of a Gaussian density field. However, the late-time density field becomes non-Gaussian due to non-linear gravitational evolution and higher-order summary statistics are required to capture all of its cosmological information. Using a Fisher formalism based on halo catalogues from the Quijote simulations, we explore the possibility of retrieving this information using the density-split clustering (DS) method, which combines clustering statistics from regions of different environmental density. We show that DS provides more precise constraints on the parameters of the $νΛ$CDM model compared to the 2PCF, and we provide suggestions for where the extra information may come from. DS improves the constraints on the sum of neutrino masses by a factor of $7$ and by factors of 4, 3, 3, 6, and 5 for $Ω_{\rm m}$, $Ω_{\rm b}$, $h$, $n_s$, and $σ_8$, respectively. We compare DS statistics when the local density environment is estimated from the real or redshift-space positions of haloes. The inclusion of DS autocorrelation functions, in addition to the cross-correlation functions between DS environments and haloes, recovers most of the information that is lost when using the redshift-space halo positions to estimate the environment. We discuss the possibility of constructing simulation-based methods to model DS clustering statistics in different scenarios.
Author's accepted manuscript
DOI: 10.48550/arxiv.2209.04310
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad59e7fc430e237f11695dceea9a66a5
حقوق: EMBARGO
رقم الأكسشن: edsair.doi.dedup.....ad59e7fc430e237f11695dceea9a66a5
قاعدة البيانات: OpenAIRE
الوصف
DOI:10.48550/arxiv.2209.04310