SUNBIRD: A simulation-based model for full-shape density-split clustering

Combining galaxy clustering information from regions of different environmental densities can help break cosmological parameter degeneracies and access non-Gaussian information from the density field that is not readily captured by the standard two-point correlation function (2PCF) analyses. However...

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Veröffentlicht in:arXiv.org 2023-10
Hauptverfasser: Cuesta-Lazaro, Carolina, Paillas, Enrique, Yuan, Sihan, Yan-Chuan Cai, Seshadri Nadathur, Percival, Will J, Beutler, Florian, de Mattia, Arnaud, Eisenstein, Daniel, ero-Sanchez, Daniel, Padilla, Nelson, Pinon, Mathilde, Ruhlmann-Kleider, Vanina, Sánchez, Ariel G, Valogiannis, Georgios, Zarrouk, Pauline
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Sprache:eng
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Zusammenfassung:Combining galaxy clustering information from regions of different environmental densities can help break cosmological parameter degeneracies and access non-Gaussian information from the density field that is not readily captured by the standard two-point correlation function (2PCF) analyses. However, modelling these density-dependent statistics down to the non-linear regime has so far remained challenging. We present a simulation-based model that is able to capture the cosmological dependence of the full shape of the density-split clustering (DSC) statistics down to intra-halo scales. Our models are based on neural-network emulators that are trained on high-fidelity mock galaxy catalogues within an extended-\(\Lambda\)CDM framework, incorporating the effects of redshift-space, Alcock-Paczynski distortions and models of the halo-galaxy connection. Our models reach sub-percent level accuracy down to \(1\,h^{-1}{\rm Mpc}\) and are robust against different choices of galaxy-halo connection modelling. When combined with the galaxy 2PCF, DSC can tighten the constraints on \(\omega_{\rm cdm}\), \(\sigma_8\), and \(n_s\) by factors of 2.9, 1.9, and 2.1, respectively, compared to a 2PCF-only analysis. DSC additionally puts strong constraints on environment-based assembly bias parameters. Our code is made publicly available on Github.
ISSN:2331-8422