Constraining νΛCDM with density-split clustering. Issue 1 (5th April 2023)

• Record Type: Journal Article
• Authors: Paillas, Enrique; Cuesta-Lazaro, Carolina; Zarrouk, Pauline; Cai, Yan-Chuan; Percival, Will J; Nadathur, Seshadri; Pinon, Mathilde; de Mattia, Arnaud; Beutler, Florian
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 522:Issue 1(2023)
• Page Start: 606
• Page End: 625
• Publication Date: 2023-04-05
• Subjects: cosmological parameters -- large-scale structure of Universe; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stad1017
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: 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 νΛCDM 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 Ωm, Ωb, h, ns, and σ8, 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.