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Geant4Reweight is an open-source C++ framework that allows users to 1) weight tracks produced by the GEANT4 particle transport Monte Carlo simulation according to hadron interaction cross section variations and 2) estimate uncertainties in GEANT4 interaction models by comparing the simulations hadron interaction cross section predictions to data. The ability to weight hadron transport as simulated by GEANT4 is crucial to the propagation of systematic uncertainties related to secondary hadronic interactions in current and upcoming neutrino oscillation experiments, including MicroBooNE, NOvA, and DUNE, as well as hadron test beam experiments such as ProtoDUNE. We provide motivation for weighting hadron tracks in GEANT4 in the context of systematic uncertainty propagation, a description of GEANT4s transport simulation technique, and a description of our weighting technique and fitting framework in the momentum range 0--10 GeV/c, which is typical for the hadrons produced by neutrino interactions in these experiments.
Recent statistical evaluations for High-Energy Physics measurements, in particular those at the Large Hadron Collider, require careful evaluation of many sources of systematic uncertainties at the same time. While the fundamental aspects of the stati
texttt{GooStats} is a software framework that provides a flexible environment and common tools to implement multi-variate statistical analysis. The framework is built upon the texttt{CERN ROOT}, texttt{MINUIT} and texttt{GooFit} packages. Running a m
GELATIO is a new software framework for advanced data analysis and digital signal processing developed for the GERDA neutrinoless double beta decay experiment. The framework is tailored to handle the full analysis flow of signals recorded by high pur
This article presents the motivation for developing a comprehensive modeling framework in which different models and parameter inputs can be compared and evaluated for a large range of jet-quenching observables measured in relativistic heavy-ion coll
Evaluated nuclear data uncertainties are often perceived as unrealistic, most often because they are thought to be too small. The impact of this issue in applied nuclear science has been discussed widely in recent years. Commonly suggested causes are