ABSTRACT
This Letter presents the results from the MiniBooNE experiment within a full "3+1" scenario where one sterile neutrino is introduced to the three-active-neutrino picture. In addition to electron-neutrino appearance at short baselines, this scenario also allows for disappearance of the muon-neutrino and electron-neutrino fluxes in the Booster Neutrino Beam, which is shared by the MicroBooNE experiment. We present the 3+1 fit to the MiniBooNE electron-(anti)neutrino and muon-(anti)neutrino data alone and in combination with MicroBooNE electron-neutrino data. The best-fit parameters of the combined fit with the exclusive charged-current quasielastic analysis (inclusive analysis) are Δm^{2}=0.209 eV^{2}(0.033 eV^{2}), |U_{e4}|^{2}=0.016(0.500), |U_{µ4}|^{2}=0.500(0.500), and sin^{2}(2θ_{µe})=0.0316(1.0). Comparing the no-oscillation scenario to the 3+1 model, the data prefer the 3+1 model with a Δχ^{2}/d.o.f.=24.7/3(17.3/3), a 4.3σ(3.4σ) preference assuming the asymptotic approximation given by Wilks's theorem.
ABSTRACT
In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons, and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded. In practice, this means that data from surface liquid argon time projection chambers will be dominated by cosmic particles, both as a source of event triggers and as the majority of the particle count in true neutrino-triggered events. In this work, we demonstrate a novel application of deep learning techniques to remove these background particles by applying deep learning on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, on a pixel-by-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.
ABSTRACT
The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.
