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Abstract:

This Letter presents an investigation of low-energy electron-neutrino interactions in the Fermilab Booster Neutrino Beam by the MicroBooNE experiment, motivated by the excess of electron-neutrino-like events observed by the MiniBooNE experiment. This is the first measurement to use data from all five years of operation of the MicroBooNE experiment, corresponding to an exposure of $1.11×{10}^{21}$ protons on target, a 70% increase on past results. Two samples of electron neutrino interactions without visible pions are used, one with visible protons and one without any visible protons. The MicroBooNE data show reasonable agreement with the nominal prediction, with $p$ values $â‰\yen 26.7\%$ when the two ${\mathrm{ν}}_e$ samples are combined, though the prediction exceeds the data in limited regions of phase space. The data are further compared to two empirical models that modify the predicted rate of electron-neutrino interactions in different variables in the simulation to match the unfolded MiniBooNE low energy excess. In the first model, this unfolding is performed as a function of electron neutrino energy, while the second model aims to match the observed shower energy and angle distributions of the MiniBooNE excess. This measurement excludes an electronlike interpretation of the MiniBooNE excess based on these models at $>99\%{\mathrm{CL}}_{\mathrm{s}}$ in all kinematic variables.

Abstract:

<jats:p>Understanding electron neutrino interactions is crucial for measurements of neutrino oscillations and searches for new physics in neutrino experiments. We present the first measurement of the flux-averaged <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"><a:mrow><a:msub><a:mrow><a:mi>ν</a:mi></a:mrow><a:mrow><a:mi>e</a:mi></a:mrow></a:msub><a:mo>+</a:mo><a:msub><a:mrow><a:mover accent="true"><a:mrow><a:mi>ν</a:mi></a:mrow><a:mrow><a:mo stretchy="false">¯</a:mo></a:mrow></a:mover></a:mrow><a:mrow><a:mi>e</a:mi></a:mrow></a:msub></a:mrow></a:math> charged-current single charged-pion production cross section on argon using the MicroBooNE detector and data from the NuMI neutrino beam. The total cross section is measured to be <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" display="inline"><e:mrow><e:mo stretchy="false">[</e:mo><e:mn>0.93</e:mn><e:mo>±</e:mo><e:mn>0.13</e:mn><e:mo stretchy="false">(</e:mo><e:mi>stat</e:mi><e:mo stretchy="false">)</e:mo><e:mo>±</e:mo><e:mn>0.27</e:mn><e:mo stretchy="false">(</e:mo><e:mi>syst</e:mi><e:mo stretchy="false">)</e:mo><e:mo stretchy="false">]</e:mo><e:mo>×</e:mo><e:msup><e:mrow><e:mn>10</e:mn></e:mrow><e:mrow><e:mo>−</e:mo><e:mn>39</e:mn></e:mrow></e:msup><e:mtext> </e:mtext><e:mtext> </e:mtext><e:msup><e:mrow><e:mi>cm</e:mi></e:mrow><e:mrow><e:mn>2</e:mn></e:mrow></e:msup><e:mo>/</e:mo><e:mi>nucleon</e:mi></e:mrow></e:math> at a mean <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" display="inline"><m:mrow><m:msub><m:mrow><m:mi>ν</m:mi></m:mrow><m:mrow><m:mi>e</m:mi></m:mrow></m:msub><m:mo>+</m:mo><m:msub><m:mrow><m:mover accent="true"><m:mrow><m:mi>ν</m:mi></m:mrow><m:mrow><m:mo stretchy="false">¯</m:mo></m:mrow></m:mover></m:mrow><m:mrow><m:mi>e</m:mi></m:mrow></m:msub></m:mrow></m:math> energy of 730 MeV. Differential cross sections are also reported in electron energy, electron and pion angles, and electron-pion opening angle.</jats:p>

Abstract:

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours.