2006/07/31 by M. C. González-García, M. C. Gonzalez-Garcia, Michele Maltoni +3 · 54 citations
Mathematics · Physics and Astronomy · #Astrophysics #Astrophysics and Cosmic Phenomena #COSMIC cancer database #Cosmic ray #Mathematics #Monte Carlo method #Neutrino #Neutrino Physics Research #Neutrino detector #Neutrino oscillation #Nuclear physics #Particle physics #Particle physics theoretical and experimental studies #Physics #Statistics #hep-ph
paper · pdf · doi:10.1088/1126-6708/2006/10/075
published in Journal of High Energy Physics 2006(10), 075 (Springer Nature) · 31 pages, 12 figures. Version to appear in JHEP
arxiv created 2006/10/11 · openalex publication_date 2006/10/26 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
The precise knowledge of the atmospheric neutrino fluxes is a key ingredient in the interpretation of the results from any atmospheric neutrino experiment. In the standard atmospheric neutrino data analysis, these fluxes are theoretical inputs obtained from sophisticated numerical calculations based on the convolution of the primary cosmic ray spectrum with the expected yield of neutrinos per incident cosmic ray. In this work we present an alternative approach to the determination of the atmospheric neutrino fluxes based on the direct extraction from the experimental data on neutrino event rates. The extraction is achieved by means of a combination of artificial neural networks as interpolants and Monte Carlo methods for faithful error estimation