en
Scientific article
Open access
English

Exhaustive neural importance sampling applied to Monte Carlo event generation

Published inPhysical review. D, vol. 102, no. 1, 013003
Publication date2020-07-16
First online date2020-07-16
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.

eng
Keywords
  • Electroweak interactions
  • Neutrino: oscillation
  • Numerical calculations: Monte Carlo
  • Neutrino nucleus: interaction
  • Channel cross section
  • Density
  • Flow
  • Programming
  • Data analysis method
NoteAppeared at the ICML 2020 Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (INNF+ 2020)
Citation (ISO format)
PINA-OTEY, Sebastian et al. Exhaustive neural importance sampling applied to Monte Carlo event generation. In: Physical review. D, 2020, vol. 102, n° 1, p. 013003. doi: 10.1103/physrevd.102.013003
Main files (1)
Article (Published version)
Identifiers
ISSN of the journal2470-0010
57views
17downloads

Technical informations

Creation10/06/2022 11:17:00 AM
First validation10/06/2022 11:17:00 AM
Update time03/16/2023 7:58:40 AM
Status update03/16/2023 7:58:39 AM
Last indexation05/06/2024 11:45:48 AM
All rights reserved by Archive ouverte UNIGE and the University of GenevaunigeBlack