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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 

Abstract Data preservation is a mandatory specification for any present and future experimental facility and it is a cost-effective way of doing fundamental research by exploiting unique data sets in the light of the continuously increasing theoretical understanding. This document summarizes the status of data preservation in high energy physics. The paradigms and the methodological advances are discussed from a perspective of more than ten years of experience with a structured effort at international level. The status and the scientific return related to the preservation of data accumulated at large collider experiments are presented, together with an account of ongoing efforts to ensure long-term analysis capabilities for ongoing and future experiments. Transverse projects aimed at generic solutions, most of which are specifically inspired by open science and FAIR principles, are presented as well. A prospective and an action plan are also indicated. 

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. 

The 2x2 Demonstrator, a prototype for the Deep Underground Neutrino Experiment (DUNE) liquid argon (LAr) Near Detector, was exposed to the Neutrinos from the Main Injector (NuMI) neutrino beam at Fermi National Accelerator Laboratory (Fermilab). This detector is a prototype of a new modular design for a liquid argon time-projection chamber (LArTPC), comprising a two-by-two array of four modules, each further segmented into two optically isolated LArTPCs. The 2x2 Demonstrator features a number of pioneering technologies, including a low-profile resistive field shell to establish drift fields, native 3D ionization pixelated imaging, and a high-coverage dielectric light readout system. The 2.4-tonne active mass detector is flanked upstream and downstream by supplemental solid-scintillator tracking planes, repurposed from the MINERvA experiment, which track ionizing particles exiting the argon volume. The antineutrino beam data collected by the detector over a 4.5 day period in 2024 include over 30,000 neutrino interactions in the LAr active volume—the first neutrino interactions reported by a DUNE detector prototype. During its physics-quality run, the 2x2 Demonstrator operated at a nominal drift field of 500 V/cm and maintained good LAr purity, with a stable electron lifetime of approximately 1.25 ms. This paper describes the detector and supporting systems, summarizes the installation and commissioning, and presents the initial validation of collected NuMI beam and off-beam self-triggers. In addition, it highlights observed interactions in the detector volume, including candidate muon antineutrino events. 

The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment with a rich physics program that includes searches for the hypothetical phenomenon of proton decay. Utilizing liquid-argon time-projection chamber technology, DUNE is expected to achieve world-leading sensitivity in the proton decay channels that involve charged kaons in their final states. The first DUNE demonstrator, ProtoDUNE Single-Phase, was a 0.77 kt detector that operated from 2018 to 2020 at the CERN Neutrino Platform, exposed to a mixed hadron and electron test-beam with momenta ranging from 0.3 to $7\mathrm{GeV}/\mathrm{c}$. We present a selection of low-energy kaons among the secondary particles produced in hadronic reactions, using data from the 6 and $7\mathrm{GeV}/\mathrm{c}$beam runs. The selection efficiency is 1% and the sample purity 92%. The initial energies of the selected kaon candidates encompass the expected energy range of kaons originating from proton decay events in DUNE (below $\sim 200\mathrm{MeV}$). In addition, we demonstrate the capability of this detector technology to discriminate between kaons and other particles such as protons and muons, and provide a comprehensive description of their energy loss in liquid argon, which shows good agreement with the simulation. These results pave the way for future proton decay searches at DUNE. 

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.