Accurate tracer particles of baryon dynamics in the adaptive mesh refinement code Ramses
Institut d’Astrophysique de Paris, CNRS & UPMC, UMR 7095, 98 bis Boulevard Arago, 75014 Paris, France
2 Korea Institute of Advanced Studies (KIAS), 85 Hoegiro, Dongdaemun-gu, 02455, Seoul, Republic of Korea
Accepted: 9 November 2018
We present a new implementation of the tracer particles algorithm based on a Monte Carlo approach for the Eulerian adaptive mesh refinement code RAMSES. The purpose of tracer particles is to keep track of where fluid elements originate in Eulerian mesh codes, so as to follow their Lagrangian trajectories and re-processing history. We provide a comparison to the more commonly used velocity-based tracer particles, and show that the Monte Carlo approach reproduces the gas distribution much more accurately. We present a detailed statistical analysis of the properties of the distribution of tracer particles in the gas and report that it follows a Poisson law. We extend these Monte Carlo gas tracer particles to tracer particles for the stars and black holes, so that they can exchange mass back and forth between themselves. With such a scheme, we can follow the full cycle of baryons, that is, from gas-forming stars to the release of mass back to the surrounding gas multiple times, or accretion of gas onto black holes. The overall impact on computation time is ∼3% per tracer per initial cell. As a proof of concept, we study an astrophysical science case – the dual accretion modes of galaxies at high redshifts –, which highlights how the scheme yields information hitherto unavailable. These tracer particles will allow us to study complex astrophysical systems where both efficiency of shock-capturing Godunov schemes and a Lagrangian follow-up of the fluid are required simultaneously.
Key words: hydrodynamics / methods: numerical / cosmology: theory / Galaxy: formation
© ESO 2019
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.