A Hydro-Particle-Mesh Code for Efficient and Rapid Simulations of the Intracluster Medium

TL;DR

The paper introduces HYPER, a fast and efficient hydro-particle-mesh code for simulating the intracluster medium, extending previous methods to higher densities and providing versatile outputs for astrophysical research.

Contribution

The paper develops an updated HPM algorithm that separately tracks gas and dark matter, enabling rapid, accurate simulations of the intracluster medium with flexible physical models.

Findings

HYPER results agree with halo model expectations for density, temperature, and pressure profiles.

Simulated galaxy cluster scaling relations match mean predictions with realistic scatter.

HYPER produces comprehensive lightcone catalogs and full-sky maps useful for various astrophysical analyses.

Abstract

We introduce the cosmological HYPER code based on an innovative hydro-particle-mesh (HPM) algorithm for efficient and rapid simulations of gas and dark matter. For the HPM algorithm, we update the approach of Gnedin & Hui (1998) to expand the scope of its application from the lower-density intergalactic medium (IGM) to the higher-density intracluster medium (ICM). While the original algorithm tracks only one effective particle species, the updated version separately tracks the gas and dark matter particles as they do not exactly trace each other on small scales. For the approximate hydrodynamics solver, the pressure term in the gas equations of motion is calculated using robust physical models. In particular, we use a dark matter halo model, ICM pressure profile, and IGM temperature-density relation, all of which can be systematically varied for parameter-space studies. We show that the HYPER simulation results are in good agreement with the halo model expectations for the density, temperature, and pressure radial profiles. Simulated galaxy cluster scaling relations for Sunyaev-Zel'dovich (SZ) and X-ray observables are also in good agreement with mean predictions, with scatter comparable to that found in hydrodynamic simulations. HYPER also produces lightcone catalogs of dark matter halos and full-sky tomographic maps of the lensing convergence, SZ effect, and X-ray emission. These simulation products are useful for testing data analysis pipelines, generating training data for machine learning, understanding selection and systematic effects, and for interpreting astrophysical and cosmological constraints.