New CC0π GENIE model tune for MicroBooNE
Metadatos
Afficher la notice complèteEditorial
American Physical Society
Date
2022-04-04Referencia bibliográfica
Abratenko, P... [et al.] (2022). New CC 0 π GENIE model tune for MicroBooNE. Physical Review D, 105(7), 072001. DOI: [10.1103/PhysRevD.105.072001]
Patrocinador
Fermi Research Alliance, LLC (FRA) DE-AC02-07CH11359; United States Department of Energy (DOE); National Science Foundation (NSF); Swiss National Science Foundation (SNSF); European Commission; UK Research & Innovation (UKRI); Science & Technology Facilities Council (STFC); Royal Society of London; European Commission; Albert Einstein Center for Fundamental Physics, Bern, SwitzerlandRésumé
Obtaining a high-quality interaction model with associated uncertainties is essential for neutrino
experiments studying oscillations, nuclear scattering processes, or both. As a primary input to the
MicroBooNE experiment’s next generation of neutrino cross section measurements and its flagship
investigation of the MiniBooNE low-energy excess, we present a new tune of the charged-current pionless
(CC0π) interaction cross section via the two major contributing processes—charged-current quasielastic
and multinucleon interaction models—within version 3.0.6 of the GENIE neutrino event generator.
Parameters in these models are tuned to muon neutrino CC0π cross section data obtained by the T2K
experiment, which provides an independent set of neutrino interactions with a neutrino flux in a similar
energy range to MicroBooNE’s neutrino beam. Although the fit is to muon neutrino data, the information
carries over to electron neutrino simulation because the same underlying models are used in GENIE.
A number of novel fit parameters were developed for this work, and the optimal parameters were chosen
from existing and new sets. We choose to fit four parameters that have not previously been constrained by
theory or data. Thus, this will be called a theory-driven tune. The result is an improved match to the T2K
CC0π data with more well-motivated uncertainties based on the fit.