Exhaustive Neural Importance Sampling applied to Monte Carlo event generation

TL;DR

This paper introduces Exhaustive Neural Importance Sampling (ENIS), a novel method using normalizing flows to improve the efficiency of Monte Carlo event generation for neutrino physics.

Contribution

The paper presents ENIS, a new neural importance sampling technique that automates proposal density selection, enhancing Monte Carlo efficiency in complex physics simulations.

Findings

ENIS improves sampling efficiency in neutrino event generation.

The method addresses rejection sampling challenges effectively.

ENIS demonstrates potential for broader applications in Monte Carlo methods.

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.