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.
Keywords
Cite
@article{arxiv.2005.12719,
title = {Exhaustive Neural Importance Sampling applied to Monte Carlo event generation},
author = {Sebastian Pina-Otey and Federico Sánchez and Thorsten Lux and Vicens Gaitan},
journal= {arXiv preprint arXiv:2005.12719},
year = {2020}
}
Comments
Published 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)