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The first simultaneous measurements of the ${\nu }_{\mu }$ quasielasticlike cross section on C, CH, ${\mathrm{H}}_2\mathrm{O}$ , Fe, and Pb targets as a function of kinematic imbalance variables in the plane transverse to the incoming neutrino direction are presented. These variables combine the muon and proton information to provide a new way to disentangle the effects of the nucleus in quasielasticlike processes. The data were obtained using a wideband ${\nu }_{\mu }$ beam with $⟨{\mathrm{E}}_{\nu }⟩\sim 6\mathrm{GeV}$ . Cross-section ratios of the different target materials to CH are also shown. These measurements are used to explore the nature of the cross-section $A$ scaling, as well as initial and final state interaction effects. Comparisons are made to predictions from a number of commonly used neutrino Monte Carlo event generators. The range of predictions of the different models tends to cover the data but the degree and consistency of the agreement suffers in regions, and on higher $A$ targets, where the final state interactions are expected to be more pronounced. 

Accelerator based neutrino oscillation experiments seek to measure the relative number of electron and muon (anti)neutrinos at different $L/E$ values. However high statistics studies of neutrino interactions are almost exclusively measured using muon (anti)neutrinos since the dominant flavor of neutrinos produced by accelerator based beams are of the muon type. This work reports new measurements of electron (anti)neutrinos interactions in hydrocarbon, obtained by strongly suppressing backgrounds initiated by muon flavor (anti)neutrinos. Double differential cross sections as a function of visible energy transfer, $E_{\text{avail}}$ , and transverse momentum transfer, $p_T$ , or three momentum transfer, $q_3$ are presented. Published by the American Physical Society2024 

Neutron production in antineutrino interactions can lead to bias in energy reconstruction in neutrino oscillation experiments, but these interactions have rarely been studied. MINERvA previously studied neutron production at an average antineutrino energy of ∼3 GeV in 2016 and found deficiencies in leading models. In this paper, the MINERvA 6 GeV average antineutrino energy dataset is shown to have similar disagreements. A measurement of the cross section for an antineutrino to produce two or more neutrons and have low visible energy is presented as an experiment-independent way to explore neutron production modeling. This cross section disagrees with several leading models’ predictions. Neutron modeling techniques from nuclear physics are used to quantify neutron detection uncertainties on this result. 

We present measurements of the cross section for antineutrino charged-current quasielasticlike scattering on hydrocarbon using the medium energy NuMI wide-band neutrino beam peaking at antineutrino energy hE¯νi ∼ 6 GeV. The measurements are presented as a function of the longitudinal momentum (pjj) and transverse momentum (pT) of the final state muon. This work complements our previously reported high statistics measurement in the neutrino channel and extends the previous antineutrino measurement made in a low energy beam at hE¯νi ∼ 3.5 GeV out to pT of 2.5 GeV=c. Current theoretical models do not completely describe the data in this previously unexplored high pT region. The single differential cross section as a function of four-momentum transfer (Q2 QE) now extends to 4 GeV2 with high statistics. The cross section as a function of Q2 QE shows that the tuned simulations developed by the MINERvA Collaboration that agreed well with the low energy beam measurements do not agree as well with the medium energy beam measurements. Newer neutrino interaction models such as the GENIE v3 tunes are better able to simulate the high Q2 QE region. 

Abstract We compare different neural network architectures for machine learning algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package “Multi-node Evolutionary Neural Networks for Deep Learning” (MENNDL), developed at Oak Ridge National Laboratory. While the domain-expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed as well. There is always a trade-off between human, and computer resources for network optimization and this work suggests that automated optimization, assuming resources are available, provides a compelling way to save significant expert time.