2023
DOI: 10.48550/arxiv.2301.02970
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An emulator-based halo model in modified gravity -- I. The halo concentration-mass relation and density profile

Cheng-Zong Ruan,
Carolina Cuesta-Lazaro,
Alexander Eggemeier
et al.

Abstract: In this series of papers we present an emulator-based halo model for the non-linear clustering of galaxies in modified gravity cosmologies. In the first paper, we present emulators for the following halo properties: the halo mass function, concentration-mass relation and halo-matter cross-correlation function. The emulators are trained on data extracted from the FORGE and BRIDGE suites of N -body simulations, respectively for two modified gravity (MG) theories: f (R) gravity and the DGP model, varying three st… Show more

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Cited by 2 publications
(3 citation statements)
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“…We need to appreciate that another promising solution for analytical modeling of the MG PS is via the fast and reliable emulation techniques [e. g 30, 110, 111].For MG models, emulators have been proposed in e.g. [112][113][114][115][116]. This approach is sophisticated and promising, however, is still in its infancy, and has limitations.…”
Section: Discussionmentioning
confidence: 99%
“…We need to appreciate that another promising solution for analytical modeling of the MG PS is via the fast and reliable emulation techniques [e. g 30, 110, 111].For MG models, emulators have been proposed in e.g. [112][113][114][115][116]. This approach is sophisticated and promising, however, is still in its infancy, and has limitations.…”
Section: Discussionmentioning
confidence: 99%
“…Current MG simulations [8,9] usually take from a factor of 2 up to a factor of 10 longer than a ΛCDM one, and performing a full MCMC parameter estimation with simulations simply becomes not a feasible task, as one would need an order of 10 4 simulations, or even more, to successfully sample the parameter space and reach convergence on the chains. Therefore, emulation techniques have been put forward in the community as viable alternatives to bypass this issue in standard cosmologies , as well as in cosmologies beyond-ΛCDM [32][33][34][35][36][37]. In order to train machine learning algorithms to be used for the emulation, we still need to create a large enough training, testing and validation simulation sets, however, the total number of simulations required for these are reduced by at least two orders of magnitude, where one would need around O(10 2 ) simulations to be divided into the specified sets.…”
Section: Introductionmentioning
confidence: 99%
“…Using equations (C.26), (C.29a) and (C.29b) we findd κG eff S κG eff F S (2) i,i (q, T ) (k, ) × I (k, T ) , (C 37). whereI (k, T ) = d 3 k 1 d 3 k 2 (2π) 3 δ D (k − k 12 )…”
mentioning
confidence: 99%