forge: the f(R)-gravity cosmic emulator project – I. Introduction and matter power spectrum emulator

Abstract

We present a large suite of cosmological simulations, the forge (F-of-R Gravity Emulator) simulation suite, which is designed to build accurate emulators for cosmological observables in galaxy clustering, weak gravitational lensing, and galaxy clusters for the f(R)-gravity model. A total of 200 simulations explore the cosmological parameter space around a standard Planck cosmology with a Latin hypercube, for 50 combinations of $\bar{f}_{R0}$, Ωm, σ8, and h with all other parameters fixed. For each parameter combination, or node, we ran four independent simulations, one pair using 10243 particles in $500\, h^{-1}\, \mathrm{Mpc}$ simulation boxes to cover small scales, and another pair using 5123 simulation particles in $1.5\, h^{-1}\, \mathrm{Gpc}$ boxes for larger scales. Each pair of initial conditions is selected such that sample variance on large scales is minimized on average. In this work we present an accurate emulator for the matter power spectrum in f(R) gravity trained on forge. We have verified, using the cross-validation technique, that the emulator accuracy is better than $2.5{{\, \rm per\, cent}}$ for the majority of nodes, particularly around the centre of the explored parameter space, up to scales of $k = 10\, h \, \mathrm{Mpc}^{-1}$. We have also checked the power spectrum emulator against simulations that are not part of our training set and found excellent agreement. Due to its high accuracy on small scales, the forge matter power spectrum emulator is well suited for weak-lensing analysis and can play a key tool in constraining f(R) gravity using current and future observational data.

Improved analytical modeling of the non-linear power spectrum in modified gravity cosmologies

“…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.…”

An emulator-based halo model in modified gravity -- I. The halo concentration-mass relation and density profile

“…To construct emulators for dark matter halo properties, we use the FORGE and BRIDGE suites of N -body simulations (Arnold et al 2022), covering 49 f (R) gravity and 49 DGP models, along with 49 ΛCDM counterparts. The simulations were performed using 1024 3 dark matter particles in a cube of side 500 h −1 Mpc (hereafter the high-resolution runs, denoted HR) or 1500 h −1 Mpc (lowresolution runs, labelled LR), using the modified gravity version of the A cosmological simulation code (Springel 2010;Arnold et al 2019b;Weinberger et al 2020).…”

“…The remaining cosmological parameter is the slope of the primordial curvature power spectrum normalised at 0.05 Mpc −1 , which is ns = 0.9652 (Arnold et al 2022).…”

“…Gaussian process (GP) regression (e.g. Williams & Rasmussen 2006) (Arnold et al 2022). GP regression is nonparametric, i.e., no specific functional form is assumed.…”

“…There have been several previous works on the emulation of cosmological quantities in the ΛCDM model and its extensions, such as Heitmann et al (2006); Habib et al (2007), the Coyote Universe (Heitmann et al 2010(Heitmann et al , 2009Lawrence et al 2010), PkANN (Agarwal et al 2012, 2014, the Mira-Titan Universe (Heitmann et al 2016;Lawrence et al 2017;Bocquet et al 2020), Kwan et al (2013Kwan et al ( , 2015 To build emulators we use the machine-learning interpolation technique of neural networks, which allows us to predict halo properties for any given cosmology within the range of parameters covered by the training data set. We use the FORGE (F-Of-R Gravity Emulator) and BRIDGE (BRaneworld-Inspired D Gravity Emulator) modified gravity N -body simulations described in Arnold et al (2022), which together cover a very broad range of parameters in two MG theories: f (R) gravity and the DGP model. The emulated halo properties incorporate all the complicated effects on non-linear scales, such as the non-linear halo bias, the halo exclusion effect and the screening mechanism.…”