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G. F. R. Caceres Vera

Publications and source records attributed to G. F. R. Caceres Vera.

3 recordsLinked to original sources

Measurement of $ν_μ$ charged-current single $π^{0}$ production on hydrocarbon in the few-GeV region using MINERvA

The semi-exclusive channel $ν_μ+\textrm{CH}\rightarrowμ^{-}π^{0}+\textrm{nucleon(s)}$ is analyzed using MINERvA exposed to the low-energy NuMI $ν_μ$ beam with spectral peak at $E_ν \simeq 3$ GeV. Differential cross sections for muon momentum and production angle, $π^{0}$ kinetic energy and production angle, and for squared four-momentum transfer are reported, and the cross section $σ(E_ν)$ is obtained over the range 1.5 GeV $\leq E_ν <$ 20 GeV. Results are compared to GENIE and NuWro predictions and to published MINERvA cross sections for $ν_μ\textrm{-CC}(π^{+})$ and $\barν_μ\textrm{-CC}(π^{0})$. Disagreements between data and simulation are observed at very low and relatively high values for muon angle and for $Q^2$ that may reflect shortfalls in modeling of interactions on carbon. For $π^{0}$ kinematic distributions however, the data are consistent with the simulation and provide support for generator treatments of pion intranuclear scattering. Using signal-event subsamples that have reconstructed protons as well as $π^{0}$ mesons, the $pπ^{0}$ invariant mass distribution is obtained, and the decay polar and azimuthal angle distributions in the rest frame of the $pπ^{0}$ system are measured in the region of $Δ(1232)^+$ production, $W < 1.4$ GeV.

hep-ex↗

Antineutrino Charged-Current reactions on Hydrocarbon with Low Momentum Transfer

We report on multinucleon effects in low momentum transfer ($< 0.8$ GeV/c) anti-neutrino 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 low energy transfer and a two-nucleon knockout process are added to a relativistic Fermi gas model of quasielastic, $Δ$ 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 investigation of alternate models for antineutrino scattering off nuclei.

hep-ex↗

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.

physics.data-an↗