DarkNews: A Python-based event generator for heavy neutral lepton production in neutrino-nucleus scattering

Abstract

We introduce DarkNews, a lightweight Python -based Monte-Carlo generator for beyond-the-Standard-Model neutrino-nucleus scattering. The generator handles the production and decay of heavy neutral leptons via additional vector or scalar mediators, as well as through transition magnetic moments. DarkNews samples pre-computed neutrino-nucleus upscattering cross sections and heavy neutrino decay rates to produce dilepton and single-photon events in accelerator neutrino experiments. We present two case studies with differential distributions for models that can explain the MiniBooNE excess. The aim of this code is to aid the neutrino theory and experimental communities in performing searches and sensitivity studies for new particles produced in neutrino upscattering. Program summaryProgram Title: DarkNewsCPC Library link to program files:https://doi.org/10.17632/k9nh5fn4pw.1Developer's repository link:https://github.com/mhostert/DarkNews-generatorLicensing provisions: MITProgramming language: PythonNature of problem: In many theories beyond the Standard Model of particle physics, new light particles are introduced with masses below the 100 GeV scale. These can be produced by neutrino-nucleus interactions inside neutrino detectors, leaving a visible signature through their decay. To search for these signatures, experiments have to simulate the underlying physical processes, which requires detailed knowledge of the cross sections and decay rates in the model, as well as the ability to generate Monte Carlo events.Solution method: DarkNews provides the necessary tools to simulate the production of heavy neutral leptons in accelerator neutrino experiments, focusing on neutrino-nucleus scattering processes. The program's scope includes models with new mediators and three heavy neutral leptons, parameterizing a large class of theories through generic neutrino, quark, and lepton interactions. It generates weighted Monte-Carlo events using vegas and its pre-calculated differential rates.