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Exhaustive neural importance sampling applied to Monte Carlo event generation

Sebastian Pina-Otey1,2,*, Federico Sánchez3, Thorsten Lux2, and Vicens Gaitan1

  • 1Aplicaciones en Informática Avanzada (AIA), Sant Cugat del Vallès (Barcelona) 08172, Spain
  • 2Institut de Física d’Altes Energies (IFAE)—Barcelona Institute of Science and Technology (BIST), Bellaterra (Barcelona) 08193, Spain
  • 3University of Geneva, Section de Physique, DPNC, Geneva 1205, Switzerland

  • *pinas@aia.es
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Phys. Rev. D 102, 013003 – Published 16 July, 2020

DOI: https://doi.org/10.1103/PhysRevD.102.013003

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.

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Phys. Rev. D 102, 013003– Published 16 July, 2020
  • Received 27 May 2020
  • Accepted 2 July 2020
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