Constraining cosmological simulations with peculiar velocities: A forward-modeling approach

Numerical simulations are a key tool for deciphering the dynamics of gravitation. Yet, they fail to spatially reproduce the Universe we observe, limiting the comparison between observations and simulations to a statistical level. This is highly problematic for rare, faint, or well-studied nearby objects that are observed in a single environment. The computational cost of recovering this environment in random simulations is prohibitive for most realistic applications. We present ̋amletPM, a method that enables the constraining of initial conditions for cosmological simulations so as to produce evolved numerical universes that can be directly compared to observations of the local Universe. Such simulations are known as constrained simulations. Our method implements the field-level forward modeling of the early-time density field from sparse and noisy measurements of late-time peculiar velocities. The dynamics are integrated with a coarse particle-mesh gravity solver, probing the mildly nonlinear regime. We applied our code to the Cosmicflows-4 compilation of peculiar velocities up to z ). The resulting constrained simulations were analyzed under the framework of the local Universe model as well as an amended version that captures systematic spatial shifts in the scattering of simulated counterparts. (160,h^ -1 ̊m Mpc h^ -1 Mpc ^3 box are presented. These spatially reproduce the observed large-scale structure in the constrained volume. Special attention is given to twelve prominent nearby galaxy clusters, whose simulated counterparts are matched on criteria of mass and separation. We provide a mass estimate constrained by the dynamical environment for each cluster. Field-level forward modeling of the initial conditions can be applied to actual observations so as to produce highly constrained cosmological simulations. In its current form, this method already overtakes in quality the pipeline currently in use in the peculiar-velocity community, although systematic biases still need to be addressed. Furthermore, improving the model can be done easily thanks to the inherent flexibility of the Bayesian approach.