TY - JOUR
T1 - SUNBIRD
T2 - A simulation-based model for full-shape density-split clustering
AU - Cuesta-Lazaro, Carolina
AU - Paillas, Enrique
AU - Yuan, Sihan
AU - Cai, Yan-Chuan
AU - Nadathur, Seshadri
AU - Percival, Will J.
AU - Beutler, Florian
AU - Mattia, Arnaud de
AU - Eisenstein, Daniel J.
AU - Forero-Sanchez, Daniel
AU - Padilla, Nelson
AU - Pinon, Mathilde
AU - Ruhlmann-Kleider, Vanina
AU - Sánchez, Ariel G.
AU - Valogiannis, Georgios
AU - Zarrouk, Pauline
N1 - Submitted to MNRAS. Source code to generate the figures available in the captions
PY - 2024/7/1
Y1 - 2024/7/1
N2 - 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-ΛCDM 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 1h-1Mpc and are robust against different choices of galaxy–halo connection modelling. When combined with the galaxy 2PCF, DSC can tighten the constraints on ωcdm, σ8, and ns 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.
AB - 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-ΛCDM 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 1h-1Mpc and are robust against different choices of galaxy–halo connection modelling. When combined with the galaxy 2PCF, DSC can tighten the constraints on ωcdm, σ8, and ns 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.
KW - astro-ph.CO
KW - cosmological parameters
KW - large-scale structure of Universe
KW - UKRI
KW - STFC
KW - ST/T005009/2
U2 - 10.1093/mnras/stae1234
DO - 10.1093/mnras/stae1234
M3 - Article
SN - 0035-8711
VL - 531
SP - 3336
EP - 3356
JO - Monthly Notices of the Royal Astronomical Society
JF - Monthly Notices of the Royal Astronomical Society
IS - 3
ER -