COSEv: A collective oscillation simulation engine for neutrinos

We introduce the implementation details of the simulation code COSEv, which numerically solves a set of non-linear partial differential equations that govern the dynamics of neutrino collective flavor conversions. We systematically provide the details of both finite difference method supported by Kreiss-Oliger dissipation and finite volume method with seventh order weighted essentially non-oscillatory scheme. To ensure the reliability of the code, we perform comparison of the simulation results with theoretically obtainable solutions. In order to understand and characterize the error accumulation behavior of the implementations when neutrino self-interactions are switched on, we also analyze the evolution of the deviation of the conserved quantities for different values of simulation parameters. We report the performance of our code with both CPUs and GPUs. The public version of the COSEv package is available at https://github .com /COSEnu /COSEnu. Program summaryProgram Title: COSEvCPC Library link to program files: https://doi .org /10 .17632 /d4dhg2sths .1Developer's repository link: https://github .com /COSEnu /COSEnu Code Ocean capsule: https://codeocean .com /capsule /7929908 Licensing provisions: BSD 3-clauseProgramming language: C++, Python 3.0+Supplementary material: https://cosenu .github .io /cosenu _docs/ Nature of problem: Collective neutrino flavor oscillations is a novel but generic phenomenon that occurs in cosmological and astrophysical environments dense in neutrinos. Understanding this phenomenon requires solving the corresponding quantum kinetic transport equation, which is a set of nonlinear and hyperbolic partial differential equations.Solution method: We adopt the method of lines to solve the hyperbolic partial differential equations in our program. The spatial derivatives are evaluated using either the finite difference method supported by 3rd order Kreiss-Oliger dissipation or the finite volume method with 7th order WENO scheme. The time integration is performed using the explicit 4th order Runge-Kutta method. Parallelization with OpenMP or OpenACC is supported.(c) 2022 Elsevier B.V. All rights reserved.