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High-resolution cosmological N-body simulations are excellent tools formodelling the formation and clustering of dark matter haloes. These simulationssuggest complex physical theories of halo formation governed by a set ofeffective physical parameters. Our goal is to extract these parameters andtheir uncertainties in a Bayesian context. We make a step towards automatisingthis process by directly comparing dark matter density projection mapsextracted from cosmological simulations, with density projections generatedfrom an analytical halo model. The model is based on a toy implementation oftwo body correlation functions. To accomplish this we use marginal neural ratioestimation, an algorithm for simulation-based inference that allows marginalposteriors to be estimated by approximating marginal likelihood-to-evidenceratios with a neural network. In this case, we train a neural network with mockimages to identify the correct values of the physical parameters that produceda given image. Using the trained neural network on cosmological N-bodysimulation images we are able to reconstruct the halo mass function, togenerate mock images similar to the N-body simulation images and to identifythe lowest mass of the haloes of those images, provided that they have the sameclustering with our training data. Our results indicate that this is apromising approach in the path towards developing cosmological simulationsassisted by neural networks.

Towards reconstructing the halo clustering and halo mass function of N-body simulations using neural ratio estimation