Towards reconstructing the halo clustering and halo mass function of Nbody simulations using neural ratio estimation
Abstract
Highresolution cosmological Nbody simulations are excellent tools for modelling the formation and clustering of dark matter haloes. These simulations suggest complex physical theories of halo formation governed by a set of effective physical parameters. Our goal is to extract these parameters and their uncertainties in a Bayesian context. We make a step towards automatising this process by directly comparing dark matter density projection maps extracted from cosmological simulations, with density projections generated from an analytical halo model. The model is based on a toy implementation of two body correlation functions. To accomplish this we use marginal neural ratio estimation, an algorithm for simulationbased inference that allows marginal posteriors to be estimated by approximating marginal likelihoodtoevidence ratios with a neural network. In this case, we train a neural network with mock images to identify the correct values of the physical parameters that produced a given image. Using the trained neural network on cosmological Nbody simulation images we are able to reconstruct the halo mass function, to generate mock images similar to the Nbody simulation images and to identify the lowest mass of the haloes of those images, provided that they have the same clustering with our training data. Our results indicate that this is a promising approach in the path towards developing cosmological simulations assisted by neural networks.
 Publication:

arXiv eprints
 Pub Date:
 June 2022
 DOI:
 10.48550/arXiv.2206.11312
 arXiv:
 arXiv:2206.11312
 Bibcode:
 2022arXiv220611312D
 Keywords:

 Astrophysics  Cosmology and Nongalactic Astrophysics