Exhaustive neural importance sampling applied to Monte Carlo event generation
Sebastian Pina-Otey, F. Sánchez, T. Lux, V. Gaitan
Abstract
The generation of accurate neutrino-nucleus cross section models needed for neutrino oscillation experiments requires simultaneously the description of many degrees of freedom and precise calculations to model nuclear responses. The detailed calculation of complete models makes the Monte Carlo generators slow and impractical. We present exhaustive neural importance sampling, a method based on normalizing flows to find a suitable proposal density for rejection sampling automatically and efficiently, and discuss how this technique solves common issues of the rejection algorithm.
Topics & Concepts
Monte Carlo methodImportance samplingComputer scienceSampling (signal processing)Rejection samplingStatistical physicsAlgorithmMonte Carlo integrationDegrees of freedom (physics and chemistry)Hybrid Monte CarloMonte Carlo method in statistical physicsEvent (particle physics)Markov chain Monte CarloMathematicsPhysicsStatisticsAstrophysicsDetectorQuantum mechanicsTelecommunicationsNeutrino Physics ResearchAstrophysics and Cosmic PhenomenaParticle physics theoretical and experimental studies