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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-per cent level accuracy down to 1 h(-1 )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(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.
Frédéric Courbin, Georges Meylan, Gianluca Castignani, Maurizio Martinelli, Malte Tewes, Slobodan Ilic, Alessandro Pezzotta, Yi Wang, Richard Massey, Fabio Finelli, Marcello Farina
Frédéric Courbin, Georges Meylan, Jean-Luc Starck, Maurizio Martinelli, Julien Lesgourgues, Slobodan Ilic, Yi Wang, Richard Massey
Cheng Zhao, Ginevra Favole, Yu Yu