arXiv subjects

G. Barr

Publications and source records attributed to G. Barr.

At least 109 records · Page 6Linked to original sources

arXiv2022

Differential cross section measurement of charged current $ν_{e}$ interactions without final-state pions in MicroBooNE

In this letter we present the first measurements of an exclusive electron neutrino cross section with the MicroBooNE experiment using data from the Booster Neutrino Beamline at Fermilab. These measurements are made for a selection of charged-current electron neutrinos without final-state pions. Differential cross sections are extracted in energy and angle with respect to the beam for the electron and the leading proton. The differential cross section as a function of proton energy is measured using events with protons both above and below the visibility threshold. This is done by including a separate selection of electron neutrino events without reconstructed proton candidates in addition to those with proton candidates. Results are compared to the predictions from several modern generators, and we find the data agrees well with these models. The data shows best agreement, as quantified by $p$-value, with the generators that predict a lower overall cross section, such as GENIE v3 and NuWro.

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arXiv2022

Scintillator ageing of the T2K near detectors from 2010 to 2021

The T2K experiment widely uses plastic scintillator as a target for neutrino interactions and an active medium for the measurement of charged particles produced in neutrino interactions at its near detector complex. Over 10 years of operation the measured light yield recorded by the scintillator based subsystems has been observed to degrade by 0.9--2.2\% per year. Extrapolation of the degradation rate through to 2040 indicates the recorded light yield should remain above the lower threshold used by the current reconstruction algorithms for all subsystems. This will allow the near detectors to continue contributing to important physics measurements during the T2K-II and Hyper-Kamiokande eras. Additionally, work to disentangle the degradation of the plastic scintillator and wavelength shifting fibres shows that the reduction in light yield can be attributed to the ageing of the plastic scintillator.

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arXiv2022

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation.

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arXiv2022

Search for an anomalous excess of charged-current quasi-elastic $ν_e$ interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction

We present a measurement of the $ν_e$-interaction rate in the MicroBooNE detector that addresses the observed MiniBooNE anomalous low-energy excess (LEE). The approach taken isolates neutrino interactions consistent with the kinematics of charged-current quasi-elastic (CCQE) events. The topology of such signal events has a final state with 1 electron, 1 proton, and 0 mesons ($1e1p$). Multiple novel techniques are employed to identify a $1e1p$ final state, including particle identification that use two methods of deep-learning-based image identification, and event isolation using a boosted decision-tree ensemble trained to recognize two-body scattering kinematics. This analysis selects 25 $ν_e$-candidate events in the reconstructed neutrino energy range of 200--1200\,MeV, while $29.0 \pm 1.9_\text{(sys)} \pm 5.4_\text{(stat)}$ are predicted when using $ν_μ$ CCQE interactions as a constraint. We use a simplified model to translate the MiniBooNE LEE observation into a prediction for a $ν_e$ signal in MicroBooNE. A $Δχ^2$ test statistic, based on the combined Neyman--Pearson $χ^2$ formalism, is used to define frequentist confidence intervals for the LEE signal strength. Using this technique, in the case of no LEE signal, we expect this analysis to exclude a normalization factor of 0.75 (0.98) times the median MiniBooNE LEE signal strength at 90\% ($2σ$) confidence level, while the MicroBooNE data yield an exclusion of 0.25 (0.38) times the median MiniBooNE LEE signal strength at 90\% ($2σ$) confidence

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arXiv2022

Search for Neutrino-Induced Neutral Current $Δ$ Radiative Decay in MicroBooNE and a First Test of the MiniBooNE Low Energy Excess Under a Single-Photon Hypothesis

We report results from a search for neutrino-induced neutral current (NC) resonant $Δ$(1232) baryon production followed by $Δ$ radiative decay, with a $\langle0.8\rangle$~GeV neutrino beam. Data corresponding to MicroBooNE's first three years of operations (6.80$\times$10$^{20}$ protons on target) are used to select single-photon events with one or zero protons and without charged leptons in the final state ($1\gamma1p$ and $1\gamma0p$, respectively). The background is constrained via an in-situ high-purity measurement of NC $π^0$ events, made possible via dedicated $2\gamma1p$ and $2\gamma0p$ selections. A total of 16 and 153 events are observed for the $1\gamma1p$ and $1\gamma0p$ selections, respectively, compared to a constrained background prediction of $20.5 \pm 3.65 \text{(sys.)} $ and $145.1 \pm 13.8 \text{(sys.)} $ events. The data lead to a bound on an anomalous enhancement of the normalization of NC $Δ$ radiative decay of less than $2.3$ times the predicted nominal rate for this process at the 90% confidence level (CL). The measurement disfavors a candidate photon interpretation of the MiniBooNE low-energy excess as a factor of $3.18$ times the nominal NC $Δ$ radiative decay rate at the 94.8% CL, in favor of the nominal prediction, and represents a greater than $50$-fold improvement over the world's best limit on single-photon production in NC interactions in the sub-GeV neutrino energy range

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arXiv2022

Novel Approach for Evaluating Detector-Related Uncertainties in a LArTPC Using MicroBooNE Data

Primary challenges for current and future precision neutrino experiments using liquid argon time projection chambers (LArTPCs) include understanding detector effects and quantifying the associated systematic uncertainties. This paper presents a novel technique for assessing and propagating LArTPC detector-related systematic uncertainties. The technique makes modifications to simulation waveforms based on a parameterization of observed differences in ionization signals from the TPC between data and simulation, while remaining insensitive to the details of the detector model. The modifications are then used to quantify the systematic differences in low- and high-level reconstructed quantities. This approach could be applied to future LArTPC detectors, such as those used in SBN and DUNE.

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arXiv2022

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6x6x6m3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties

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arXiv2022

Cosmic ray muon clustering for the MicroBooNE liquid argon time projection chamber using sMask-RCNN

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.

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arXiv2022

Search for an anomalous excess of inclusive charged-current $ν_e$ interactions in the MicroBooNE experiment using Wire-Cell reconstruction

We report a search for an anomalous excess of inclusive charged-current (CC) $ν_e$ interactions using the Wire-Cell event reconstruction package in the MicroBooNE experiment, which is motivated by the previous observation of a low-energy excess (LEE) of electromagnetic events from the MiniBooNE experiment. With a single liquid argon time projection chamber detector, the measurements of $ν_μ$ CC interactions as well as $π^0$ interactions are used to constrain signal and background predictions of $ν_e$ CC interactions. A data set collected from February 2016 to July 2018 corresponding to an exposure of 6.369 $\times$ 10$^{20}$ protons on target from the Booster Neutrino Beam at FNAL is analyzed. With $x$ representing an overall normalization factor and referred to as the LEE strength parameter, we select 56 fully contained $ν_e$ CC candidates while expecting 69.6 $\pm$ 8.0 (stat.) $\pm$ 5.0 (sys.) and 103.8 $\pm$ 9.0 (stat.) $\pm$ 7.4 (sys.) candidates after constraints for the absence (eLEE$_{x=0}$) of the median signal strength derived from the MiniBooNE observation and the presence (eLEE$_{x=1}$) of that signal strength, respectively. Under a nested hypothesis test using both rate and shape information in all available channels, the best-fit $x$ is determined to be 0 (eLEE$_{x=0}$) with a 95.5% confidence level upper limit of $x$ at 0.502. Under a simple-vs-simple hypotheses test, the eLEE$_{x=1}$ hypothesis is rejected at 3.75$σ$, while the eLEE$_{x=0}$ hypothesis is shown to be consistent with the observation at 0.45$σ$. In the context of the eLEE model, the estimated 68.3% confidence interval of the $ν_e$ hypothesis to explain the LEE observed in the MiniBooNE experiment is disfavored at a significance level of more than 2.6$σ$ (3.0$σ$) considering MiniBooNE's full (statistical) uncertainties.

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arXiv2022

New CC0π GENIE Model Tune for MicroBooNE

A novel tune has been made for the MicroBooNE experiment. The fit uses 4 new parameters within the GENIE v3.0.6 Monte Carlo program. Charged current pionless data from the T2K experiment was used. New uncertainties were obtained. These results will be used in future MicroBooNE analyses.

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arXiv2022

Search for an Excess of Electron Neutrino Interactions in MicroBooNE Using Multiple Final State Topologies

We present a measurement of electron neutrino interactions from the Fermilab Booster Neutrino Beam using the MicroBooNE liquid argon time projection chamber to address the nature of the excess of low energy interactions observed by the MiniBooNE collaboration. Three independent electron neutrino searches are performed across multiple single electron final states, including an exclusive search for two-body scattering events with a single proton, a semi-inclusive search for pion-less events, and a fully inclusive search for events containing all hadronic final states. With differing signal topologies, statistics, backgrounds, reconstruction algorithms, and analysis approaches, the results are found to be consistent with the nominal electron neutrino rate expectations from the Booster Neutrino Beam and no excess of electron neutrino events is observed.

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arXiv2022

First Measurement of Energy-Dependent Inclusive Muon Neutrino Charged-Current Cross Sections on Argon with the MicroBooNE Detector

We report a measurement of the energy-dependent total charged-current cross section $σ\left(E_ν\right)$ for inclusive muon neutrinos scattering on argon, as well as measurements of flux-averaged differential cross sections as a function of muon energy and hadronic energy transfer ($ν$). Data corresponding to 5.3$\times$10$^{19}$ protons on target of exposure were collected using the MicroBooNE liquid argon time projection chamber located in the Fermilab Booster Neutrino Beam with a mean neutrino energy of approximately 0.8 GeV. The mapping between the true neutrino energy $E_ν$ and reconstructed neutrino energy $E^{rec}_ν$ and between the energy transfer $ν$ and reconstructed hadronic energy $E^{rec}_{had}$ are validated by comparing the data and Monte Carlo (MC) predictions. In particular, the modeling of the missing hadronic energy and its associated uncertainties are verified by a new method that compares the $E^{rec}_{had}$ distributions between data and an MC prediction after constraining the reconstructed muon kinematic distributions, energy and polar angle, to those of data. The success of this validation gives confidence that the missing energy in the MicroBooNE detector is well-modeled and underpins first-time measurements of both the total cross section $σ\left(E_ν\right)$ and the differential cross section $dσ/dν$ on argon.

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arXiv2022

Snowmass Neutrino Frontier: DUNE Physics Summary

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino oscillation experiment with a primary physics goal of observing neutrino and antineutrino oscillation patterns to precisely measure the parameters governing long-baseline neutrino oscillation in a single experiment, and to test the three-flavor paradigm. DUNE's design has been developed by a large, international collaboration of scientists and engineers to have unique capability to measure neutrino oscillation as a function of energy in a broadband beam, to resolve degeneracy among oscillation parameters, and to control systematic uncertainty using the exquisite imaging capability of massive LArTPC far detector modules and an argon-based near detector. DUNE's neutrino oscillation measurements will unambiguously resolve the neutrino mass ordering and provide the sensitivity to discover CP violation in neutrinos for a wide range of possible values of $δ_{CP}$. DUNE is also uniquely sensitive to electron neutrinos from a galactic supernova burst, and to a broad range of physics beyond the Standard Model (BSM), including nucleon decays. DUNE is anticipated to begin collecting physics data with Phase I, an initial experiment configuration consisting of two far detector modules and a minimal suite of near detector components, with a 1.2 MW proton beam. To realize its extensive, world-leading physics potential requires the full scope of DUNE be completed in Phase II. The three Phase II upgrades are all necessary to achieve DUNE's physics goals: (1) addition of far detector modules three and four for a total FD fiducial mass of at least 40 kt, (2) upgrade of the proton beam power from 1.2 MW to 2.4 MW, and (3) replacement of the near detector's temporary muon spectrometer with a magnetized, high-pressure gaseous argon TPC and calorimeter.

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arXiv2022

A Gaseous Argon-Based Near Detector to Enhance the Physics Capabilities of DUNE

This document presents the concept and physics case for a magnetized gaseous argon-based detector system (ND-GAr) for the Deep Underground Neutrino Experiment (DUNE) Near Detector. This detector system is required in order for DUNE to reach its full physics potential in the measurement of CP violation and in delivering precision measurements of oscillation parameters. In addition to its critical role in the long-baseline oscillation program, ND-GAr will extend the overall physics program of DUNE. The LBNF high-intensity proton beam will provide a large flux of neutrinos that is sampled by ND-GAr, enabling DUNE to discover new particles and search for new interactions and symmetries beyond those predicted in the Standard Model.

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arXiv2022

Electromagnetic Shower Reconstruction and Energy Validation with Michel Electrons and $π^0$ Samples for the Deep-Learning-Based Analyses in MicroBooNE

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 $ν_μ$-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.

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arXiv2022

Search for an anomalous excess of charged-current $ν_e$ interactions without pions in the final state with the MicroBooNE experiment

This article presents a measurement of $ν_e$ interactions without pions in the final state using the MicroBooNE experiment and an investigation into the excess of low-energy electromagnetic events observed by the MiniBooNE collaboration. The measurement is performed in exclusive channels with (1$e$N$p$0$π$) and without (1$e$0$p$0$π$) visible final-state protons using 6.86$\times 10^{20}$ protons on target of data collected from the Booster Neutrino Beam at Fermilab. Events are reconstructed with the Pandora pattern recognition toolkit and selected using additional topological information from the MicroBooNE liquid argon time projection chamber. Using a goodness-of-fit test the data are found to be consistent with the predicted number of events with nominal flux and interaction models with a $p$-value of 0.098 in the two channels combined. A model based on the low-energy excess observed in MiniBooNE is introduced to quantify the strength of a possible $ν_e$ excess. The analysis suggests that if an excess is present, it is not consistent with a simple scaling of the $ν_e$ contribution to the flux. Combined, the 1$e$N$p$0$π$ and 1$e$0$p$0$π$ channels do not give a conclusive indication about the tested model, but separately they both disfavor the low-energy excess model at $>$90% CL. The observation in the most sensitive 1$e$N$p$0$π$ channel is below the prediction and consistent with no excess. In the less sensitive 1$e$0$p$0$π$ channel the observation at low energy is above the prediction, while overall there is agreement over the full energy spectrum.

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arXiv2022

First Measurement of Inclusive Electron-Neutrino and Antineutrino Charged Current Differential Cross Sections in Charged Lepton Energy on Argon in MicroBooNE

We present the first measurement of the single-differential $ν_e + \barν_e$ charged-current inclusive cross sections on argon in electron or positron energy and in electron or positron scattering cosine over the full angular range. Data were collected using the MicroBooNE liquid argon time projection chamber located off-axis from the Fermilab Neutrinos at the Main Injector beam over an exposure of $2.0\times10^{20}$ protons on target. The signal definition includes a 60 MeV threshold on the $ν_e$ or $\barν_e$ energy and a 120 MeV threshold on the electron or positron energy. The measured total and differential cross sections are found to be in agreement with the GENIE, NuWro, and GiBUU neutrino generators.

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arXiv2022

Calorimetric classification of track-like signatures in liquid argon TPCs using MicroBooNE data

The MicroBooNE liquid argon time projection chamber located at Fermilab is a neutrino experiment dedicated to the study of short-baseline oscillations, the measurements of neutrino cross sections in liquid argon, and to the research and development of this novel detector technology. Accurate and precise measurements of calorimetry are essential to the event reconstruction and are achieved by leveraging the TPC to measure deposited energy per unit length along the particle trajectory, with mm resolution. We describe the non-uniform calorimetric reconstruction performance in the detector, showing dependence on the angle of the particle trajectory. Such non-uniform reconstruction directly affects the performance of the particle identification algorithms which infer particle type from calorimetric measurements. This work presents a new particle identification method which accounts for and effectively addresses such non-uniformity. The newly developed method shows improved performance compared to previous algorithms, illustrated by a 94% proton selection efficiency and a 10% muon mis-identification rate, with a fairly loose selection of tracks performed on beam data. The performance is further demonstrated by identifying exclusive final states in $ν_μ CC$ interactions. While developed using MicroBooNE data and simulation, this method is easily applicable to future LArTPC experiments, such as SBND, ICARUS, and DUNE.

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