Machine learning cosmological structure formation

TL;DR

This paper demonstrates that a machine learning algorithm trained on N-body simulations can accurately predict dark matter halo formation and properties, matching classical theoretical models without explicit physical assumptions.

Contribution

It introduces a machine learning approach that learns the relationship between initial conditions and halo formation, providing insights without relying on approximate collapse models.

Findings

Predicts halo formation consistent with spherical collapse models

Linear density field suffices for accurate predictions

Generalizes well across different initial conditions

Abstract

We train a machine learning algorithm to learn cosmological structure formation from N-body simulations. The algorithm infers the relationship between the initial conditions and the final dark matter haloes, without the need to introduce approximate halo collapse models. We gain insights into the physics driving halo formation by evaluating the predictive performance of the algorithm when provided with different types of information about the local environment around dark matter particles. The algorithm learns to predict whether or not dark matter particles will end up in haloes of a given mass range, based on spherical overdensities. We show that the resulting predictions match those of spherical collapse approximations such as extended Press-Schechter theory. Additional information on the shape of the local gravitational potential is not able to improve halo collapse predictions; the linear density field contains sufficient information for the algorithm to also reproduce ellipsoidal collapse predictions based on the Sheth-Tormen model. We investigate the algorithm's performance in terms of halo mass and radial position and perform blind analyses on independent initial conditions realisations to demonstrate the generality of our results.