Accelerator Neutrinos

Abstract:

We introduce the MINERvA Analysis Toolkit (MAT), a utility for centralizing the handling of systematic uncertainties in HEP analyses. The fundamental utilities of the toolkit are the MnvHnD, a powerful histogram container class, and the systematic Universe classes, which provide a modular implementation of the many universe error analysis approach. These products can be used stand-alone or as part of a complete error analysis prescription. They support the propagation of systematic uncertainty through all stages of analysis, and provide flexibility for an arbitrary level of user customization. This extensible solution to error analysis enables the standardization of systematic uncertainty definitions across an experiment and a transparent user interface to lower the barrier to entry for new analyzers.

Abstract:

Abstract Measurements of both the inclusive and differential production cross sections of a top-quark–antiquark pair in association with a Z boson ( $$t{\bar{t}}Z$$ t t ¯ Z ) are presented. The measurements are performed by targeting final states with three or four isolated leptons (electrons or muons) and are based on $$\sqrt{s} = 13$$ s = 13 TeV proton–proton collision data with an integrated luminosity of 139 $$\hbox {fb}^{-1}$$ fb - 1 , recorded from 2015 to 2018 with the ATLAS detector at the CERN Large Hadron Collider. The inclusive cross section is measured to be $$\sigma _{t{\bar{t}}Z} = 0.99 \pm 0.05$$ σ t t ¯ Z = 0.99 ± 0.05 (stat.) $$\pm \, 0.08$$ ± 0.08 (syst.) pb, in agreement with the most precise theoretical predictions. The differential measurements are presented as a function of a number of kinematic variables which probe the kinematics of the $$t{\bar{t}}Z$$ t t ¯ Z system. Both absolute and normalised differential cross-section measurements are performed at particle and parton levels for specific fiducial volumes and are compared with theoretical predictions at different levels of precision, based on a $$\chi ^{2}/$$ χ 2 / ndf and p value computation. Overall, good agreement is observed between the unfolded data and the predictions.