Prediction of Individual Halo Concentrations Across Cosmic Time Using Neural Networks

TL;DR

This paper develops a neural network model that accurately predicts individual dark matter halo concentrations across different redshifts using their mass accretion histories, outperforming previous models.

Contribution

The study introduces a neural network approach trained on cosmological simulation data to predict halo concentrations, demonstrating improved accuracy over existing models.

Findings

Model achieves lower RMS error than Zhao et al. and Giocoli et al.

Effective across multiple cosmological simulations.

Enables rapid predictions of halo concentrations.

Abstract

The concentration of dark matter haloes is closely linked to their mass accretion history. We utilize the halo mass accretion histories from large cosmological N-body simulations as inputs for our neural networks, which we train to predict the concentration of individual haloes at a given redshift. The trained model performs effectively in other cosmological simulations, achieving the root mean square error between the actual and predicted concentrations that significantly lower than that of the model by Zhao et al. and Giocoli et al. at any redshift. This model serves as a valuable tool for rapidly predicting halo concentrations at specified redshifts in large cosmological simulations.