Abstract
Wire-Cell is a 3D event reconstruction package for liquid
argon time projection chambers. Through geometry, time, and drifted
charge from multiple readout wire planes, 3D space points with
...associated charge are reconstructed prior to the pattern recognition
stage. Pattern recognition techniques, including track trajectory
and d
Q
/d
x
(ionization charge per unit length) fitting, 3D neutrino
vertex fitting, track and shower separation, particle-level
clustering, and particle identification are then applied on these 3D
space points as well as the original 2D projection measurements. A
deep neural network is developed to enhance the reconstruction of
the neutrino interaction vertex. Compared to traditional
algorithms, the deep neural network boosts the vertex efficiency by
a relative 30% for charged-current ν
e
interactions. This
pattern recognition achieves 80–90% reconstruction efficiencies
for primary leptons, after a 65.8% (72.9%) vertex efficiency for
charged-current ν
e
(ν
μ
) interactions. Based on the
resulting reconstructed particles and their kinematics, we also
achieve 15-20% energy reconstruction resolutions for
charged-current neutrino interactions.
Abstract
The MicroBooNE liquid argon time projection chamber (LArTPC) maintains a high level of liquid argon purity through the use of a filtration system that removes electronegative contaminants in ...continuously-circulated liquid, recondensed boil off, and externally supplied argon gas. We use the MicroBooNE LArTPC to reconstruct MeV-scale radiological decays. Using this technique we measure the liquid argon filtration system's efficacy at removing radon. This is studied by placing a 500 kBq
222
Rn source upstream of the filters and searching for a time-dependent increase in the number of radiological decays in the LArTPC. In the context of two models for radon mitigation via a liquid argon filtration system, a slowing mechanism and a trapping mechanism, MicroBooNE data supports a radon reduction factor of greater than 97% or 99.999%, respectively. Furthermore, a radiological survey of the filters found that the copper-based filter material was the primary medium that removed the
222
Rn. This is the first observation of radon mitigation in liquid argon with a large-scale copper-based filter and could offer a radon mitigation solution for future large LArTPCs.
Abstract
In this article, we describe a modified implementation of Mask Region-based Convolutional Neural Networks (Mask-RCNN) for cosmic ray muon clustering in a liquid argon TPC and applied to ...MicroBooNE neutrino data. Our implementation of this network, called sMask-RCNN, uses sparse submanifold convolutions to increase processing speed on sparse datasets, and is compared to the original dense version in several metrics. The networks are trained to use wire readout images from the MicroBooNE liquid argon time projection chamber as input and produce individually labeled particle interactions within the image. These outputs are identified as either cosmic ray muon or electron neutrino interactions. We find that sMask-RCNN has an average pixel clustering efficiency of 85.9% compared to the dense network's average pixel clustering efficiency of 89.1%. We demonstrate the ability of sMask-RCNN used in conjunction with MicroBooNE's state-of-the-art Wire-Cell cosmic tagger to veto events containing only cosmic ray muons. The addition of sMask-RCNN to the Wire-Cell cosmic tagger removes 70% of the remaining cosmic ray muon background events at the same electron neutrino event signal efficiency. This event veto can provide 99.7% rejection of cosmic ray-only background events while maintaining an electron neutrino event-level signal efficiency of 80.1%. In addition to cosmic ray muon identification, sMask-RCNN could be used to extract features and identify different particle interaction types in other 3D-tracking detectors.
We present a measurement of η production from neutrino interactions on argon with the MicroBooNE detector. The modeling of resonant neutrino interactions on argon is a critical aspect of the neutrino ...oscillation physics program being carried out by the DUNE and Short Baseline Neutrino programs. η production in neutrino interactions provides a powerful new probe of resonant interactions, complementary to pion channels, and is particularly suited to the study of higher-order resonances beyond the Δ(1232). We measure a flux-integrated cross section for neutrino-induced η production on argon of 3.22±0.84(stat)±0.86(syst) 10^{-41} cm^{2}/nucleon. By demonstrating the successful reconstruction of the two photons resulting from η production, this analysis enables a novel calibration technique for electromagnetic showers in GeV accelerator neutrino experiments.
Full text
Available for:
CMK, CTK, FMFMET, IJS, NUK, PNG, UL, UM
This article presents the reconstruction of the electromagnetic activity from electrons and photons (showers) used in the MicroBooNE deep learning-based low energy electron search. The reconstruction ...algorithm uses a combination of traditional and deep learning-based techniques to estimate shower energies. We validate these predictions using two νμ-sourced data samples: charged/neutral current interactions with final state neutral pions and charged current interactions in which the muon stops and decays within the detector producing a Michel electron. Both the neutral pion sample and Michel electron sample demonstrate agreement between data and simulation. Further, the absolute shower energy scale is shown to be consistent with the relevant physical constant of each sample: the neutral pion mass peak and the Michel energy cutoff.
We present a deep learning-based method for estimating the neutrino energy of charged-current neutrino-argon interactions. We employ a recurrent neural network (RNN) architecture for neutrino energy ...estimation in the MicroBooNE experiment, utilizing liquid argon time projection chamber (LArTPC) detector technology. Traditional energy estimation approaches in LArTPCs, which largely rely on reconstructing and summing visible energies, often experience sizable biases and resolution smearing because of the complex nature of neutrino interactions and the detector response. The estimation of neutrino energy can be improved after considering the kinematics information of reconstructed final-state particles. Utilizing kinematic information of reconstructed particles, the deep learning-based approach shows improved resolution and reduced bias for the muon neutrino Monte Carlo simulation sample compared to the traditional approach. In order to address the common concern about the effectiveness of this method on experimental data, the RNN-based energy estimator is further examined and validated with dedicated data-simulation consistency tests using MicroBooNE data. We also assess its potential impact on a neutrino oscillation study after accounting for all statistical and systematic uncertainties and show that it enhances physics sensitivity. This method has good potential to improve the performance of other physics analyses.
We present a measurement of neutral pion production in charged-current interactions using data recorded with the MicroBooNE detector exposed to Fermilab's booster neutrino beam. The signal comprises ...one muon, one neutral pion, any number of nucleons, and no charged pions. Studying neutral pion production in the MicroBooNE detector provides an opportunity to better understand neutrino-argon interactions, and is crucial for future accelerator-based neutrino oscillation experiments. Using a dataset corresponding to \(6.86 \times 10^{20}\) protons on target, we present single-differential cross sections in muon and neutral pion momenta, scattering angles with respect to the beam for the outgoing muon and neutral pion, as well as the opening angle between the muon and neutral pion. Data extracted cross sections are compared to generator predictions. We report good agreement between the data and the models for scattering angles, except for an over-prediction by generators at muon forward angles. Similarly, the agreement between data and the models as a function of momentum is good, except for an underprediction by generators in the medium momentum ranges, \(200-400\) MeV for muons and \(100-200\) MeV for pions.
Charged-current neutrino interactions with final states containing zero mesons and at least one proton are of high interest for current and future accelerator-based neutrino oscillation experiments. ...Using the Booster Neutrino Beam and the MicroBooNE detector at Fermi National Accelerator Laboratory, we have obtained the first double-differential cross section measurements of this channel for muon neutrino scattering on an argon target with a proton momentum threshold of 0.25 GeV/c. We also report a flux-averaged total cross section of \(\sigma = (11.8 \pm 1.2) \times 10^{-38}\) cm\(^2\) / Ar and several single-differential measurements which extend and improve upon previous results. Statistical and systematic uncertainties are quantified with a full treatment of correlations across 359 kinematic bins, including correlations between distributions describing different observables. The resulting data set provides the most detailed information obtained to date for testing models of mesonless neutrino-argon scattering.
We report the first measurement of $\pi^0$ production in neutral current (NC)
interactions on argon with average neutrino energy of $\lesssim1$~GeV. We use
data from the MicroBooNE detector's ...85-tonne active volume liquid argon time
projection chamber situated in Fermilab's Booster Neutrino Beam and exposed to
$5.89\times10^{20}$ protons on target for this measurement. Measurements of NC
$\pi^0$ events are reported for two exclusive event topologies without charged
pions. Those include a topology with two photons from the decay of the $\pi^0$
and one proton and a topology with two photons and zero protons. Flux-averaged
cross-sections for each exclusive topology and for their semi-inclusive
combination are extracted (efficiency-correcting for two-plus proton final
states), and the results are compared to predictions from the \textsc{genie},
\textsc{neut}, and \textsc{NuWro} neutrino event generators. We measure cross
sections of $1.243\pm0.185$ (syst) $\pm0.076$ (stat), $0.444\pm0.098\pm0.047$,
and $0.624\pm0.131\pm0.075$ $10^{-38}\textrm{cm}^2/\textrm{Ar}$ for the
semi-inclusive NC$\pi^0$, exclusive NC$\pi^0$+1p, and exclusive NC$\pi^0$+0p
processes, respectively.
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