Measurement of nuclear effects in neutrino interactions with minimal dependence on neutrino energy

We present a phenomenological study of nuclear effects in neutrino charged-current interactions, using transverse kinematic imbalances in exclusive measurements. Novel observables with minimal dependence on neutrino energy are proposed to study quasielastic scattering, and especially resonance production. They should be able to provide direct constraints on nuclear effects in neutrino- and antineutrino-nucleus interactions.Comment: 7 pages, 9 figures, accepted version by PR

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at Fermilab. $A$-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. We find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.Comment: 41 page

Antineutrino Charged-Current Reactions on Hydrocarbon with Low Momentum Transfer

We report on multinucleon effects in low momentum transfer (\u3c 0.8 GeV/c) antineutrino interactions on plastic (CH) scintillator. These data are from the 2010-2011 antineutrino phase of the MINERvA experiment at Fermilab. The hadronic energy spectrum of this inclusive sample is well described when a screening effect at a low energy transfer and a two-nucleon knockout process are added to a relativistic Fermi gas model of quasielastic, Delta resonance, and higher resonance processes. In this analysis, model elements introduced to describe previously published neutrino results have quantitatively similar benefits for this antineutrino sample. We present the results as a double-differential cross section to accelerate the investigation of alternate models for antineutrino scattering off nuclei

Measurement of νˉμ\bar{\nu}_{\mu} and νμ\nu_{\mu} charged current inclusive cross sections and their ratio with the T2K off-axis near detector

We report a measurement of cross section $\sigma(\nu_{\mu}+{\rm nucleus}\rightarrow\mu^{-}+X)$ and the first measurements of the cross section $\sigma(\bar{\nu}_{\mu}+{\rm nucleus}\rightarrow\mu^{+}+X)$ and their ratio $R(\frac{\sigma(\bar \nu)}{\sigma(\nu)})$ at (anti-)neutrino energies below 1.5 GeV. We determine the single momentum bin cross section measurements, averaged over the T2K $\bar{\nu}/\nu$-flux, for the detector target material (mainly Carbon, Oxygen, Hydrogen and Copper) with phase space restricted laboratory frame kinematics of $\theta_{\mu}$500 MeV/c. The results are $\sigma(\bar{\nu})=\left( 0.900\pm0.029{\rm (stat.)}\pm0.088{\rm (syst.)}\right)\times10^{-39}$ and $\sigma(\nu)=\left( 2.41\ \pm0.022{\rm{(stat.)}}\pm0.231{\rm (syst.)}\ \right)\times10^{-39}$in units of cm$^{2}$/nucleon and$R\left(\frac{\sigma(\bar{\nu})}{\sigma(\nu)}\right)= 0.373\pm0.012{\rm (stat.)}\pm0.015{\rm (syst.)}$.Comment: 18 pages, 8 figure