Exhaustive Neural Importance Sampling applied to Monte Carlo event generation

Abstract

The generation of accurate neutrino-nucleus cross-section models needed for neutrino oscillation experiments require 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 (ENIS), 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.

arXiv commentPublished in Physical Review D 102, 013003 (2020). Appeared at the ICML 2020 Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (INNF+ 2020)

References in corpus (6)

• Constraint on the Matter-Antimatter Symmetry-Violating Phase in Neutrino Oscillations
• NuSTEC White Paper: Status and Challenges of Neutrino-Nucleus Scattering
• First measurement of neutrino oscillation parameters using neutrinos and antineutrinos by NOvA
• Event Generation with Normalizing Flows
• Exploring phase space with Neural Importance Sampling
• Generalized Rejection Sampling Schemes and Applications in Signal Processing

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• Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robust Statistical Tests
• Variance Reduction via Simultaneous Importance Sampling and Control Variates Techniques Using Vegas
• Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics