Prediction of Individual Halo Concentrations Across Cosmic Time Using Neural Networks

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.

12 pages, 6 figures, version published by Universe

References in corpus (11)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Concentration, Spin and Shape of Dark Matter Haloes as a Function of the Cosmological Model: WMAP1, WMAP3 and WMAP5 results
• The redshift dependence of the structure of massive LCDM halos
• Dark matter and cosmic structure
• Large-scale dark matter simulations
• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• HBT+: an improved code for finding subhalos and building merger trees in cosmological simulations
• The galaxy size to halo spin relation of disk galaxies in cosmological hydrodynamical simulations
• The abundance of dark matter haloes down to Earth mass
• The mass accretion history of dark matter haloes down to Earth mass
• Numerical convergence of pre-initial conditions on dark matter halo properties