Papers › Magrathea-Pathfinder: A 3D adaptive-mesh code for geodesic ray tracing in N-body simulations
Magrathea-Pathfinder: A 3D adaptive-mesh code for geodesic ray tracing in N-body simulations
Michel-Andrès Breton, Vincent Reverdy
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We introduce Magrathea-Pathfinder, a relativistic ray-tracing framework that can reconstruct the past light cone of observers in cosmological simulations. The code directly computes the 3D trajectory of light rays through the null geodesic equations, with the weak-field limit as its only approximation. This approach offers high levels of versatility while removing the need for many of the standard ray-tracing approximations such as plane-parallel, Born, or multiple-lens. Moreover, the use of adaptive integration steps and interpolation strategies based on adaptive-mesh refinement (AMR) grids allows Magrathea-Pathfinder to accurately account for the non-linear regime of structure formation and fully take advantage of the small-scale gravitational clustering. To handle very large N-body simulations, the framework has been designed as a high-performance computing post-processing tool relying on a hybrid parallelization that combines MPI tasks with C++11 std::threads. In this paper, we describe how realistic cosmological observables can be computed from numerical simulation using ray-tracing techniques. We discuss in particular the production of simulated catalogues and sky maps that account for all the observational effects considering first-order metric perturbations (such as peculiar velocities, gravitational potential, integrated Sachs-Wolfe, time-delay, and gravitational lensing). We perform convergence tests of our gravitational lensing algorithms and conduct performance benchmarks of the null geodesic integration procedures. Magrathea-Pathfinder introduces sophisticated ray-tracing tools to make the link between the space of N-body simulations and light-cone observables. This should provide new ways of exploring existing cosmological probes and building new ones beyond standard assumptions in order to prepare for the next generation of large-scale structure surveys.
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