arXiv subjects

C. Barnes

Publications and source records attributed to C. Barnes.

At least 19 recordsLinked to original sources

arXiv2023

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation.

hep-ex↗

arXiv2023

Impact of cross-section uncertainties on supernova neutrino spectral parameter fitting in the Deep Underground Neutrino Experiment

A primary goal of the upcoming Deep Underground Neutrino Experiment (DUNE) is to measure the $\mathcal{O}(10)$ MeV neutrinos produced by a Galactic core-collapse supernova if one should occur during the lifetime of the experiment. The liquid-argon-based detectors planned for DUNE are expected to be uniquely sensitive to the $ν_e$ component of the supernova flux, enabling a wide variety of physics and astrophysics measurements. A key requirement for a correct interpretation of these measurements is a good understanding of the energy-dependent total cross section $σ(E_ν)$ for charged-current $ν_e$ absorption on argon. In the context of a simulated extraction of supernova $ν_e$ spectral parameters from a toy analysis, we investigate the impact of $σ(E_ν)$ modeling uncertainties on DUNE's supernova neutrino physics sensitivity for the first time. We find that the currently large theoretical uncertainties on $σ(E_ν)$ must be substantially reduced before the $ν_e$ flux parameters can be extracted reliably: in the absence of external constraints, a measurement of the integrated neutrino luminosity with less than 10\% bias with DUNE requires $σ(E_ν)$ to be known to about 5%. The neutrino spectral shape parameters can be known to better than 10% for a 20% uncertainty on the cross-section scale, although they will be sensitive to uncertainties on the shape of $σ(E_ν)$. A direct measurement of low-energy $ν_e$-argon scattering would be invaluable for improving the theoretical precision to the needed level.

hep-ex↗

arXiv2023

Identification and reconstruction of low-energy electrons in the ProtoDUNE-SP detector

Measurements of electrons from $ν_e$ interactions are crucial for the Deep Underground Neutrino Experiment (DUNE) neutrino oscillation program, as well as searches for physics beyond the standard model, supernova neutrino detection, and solar neutrino measurements. This article describes the selection and reconstruction of low-energy (Michel) electrons in the ProtoDUNE-SP detector. ProtoDUNE-SP is one of the prototypes for the DUNE far detector, built and operated at CERN as a charged particle test beam experiment. A sample of low-energy electrons produced by the decay of cosmic muons is selected with a purity of 95%. This sample is used to calibrate the low-energy electron energy scale with two techniques. An electron energy calibration based on a cosmic ray muon sample uses calibration constants derived from measured and simulated cosmic ray muon events. Another calibration technique makes use of the theoretically well-understood Michel electron energy spectrum to convert reconstructed charge to electron energy. In addition, the effects of detector response to low-energy electron energy scale and its resolution including readout electronics threshold effects are quantified. Finally, the relation between the theoretical and reconstructed low-energy electron energy spectrum is derived and the energy resolution is characterized. The low-energy electron selection presented here accounts for about 75% of the total electron deposited energy. After the addition of lost energy using a Monte Carlo simulation, the energy resolution improves from about 40% to 25% at 50~MeV. These results are used to validate the expected capabilities of the DUNE far detector to reconstruct low-energy electrons.

hep-ex↗

arXiv2023

A Race Track Trapped-Ion Quantum Processor

We describe and benchmark a new quantum charge-coupled device (QCCD) trapped-ion quantum computer based on a linear trap with periodic boundary conditions, which resembles a race track. The new system successfully incorporates several technologies crucial to future scalability, including electrode broadcasting, multi-layer RF routing, and magneto-optical trap (MOT) loading, while maintaining, and in some cases exceeding, the gate fidelities of previous QCCD systems. The system is initially operated with 32 qubits, but future upgrades will allow for more. We benchmark the performance of primitive operations, including an average state preparation and measurement error of 1.6(1)$\times 10^{-3}$, an average single-qubit gate infidelity of $2.5(3)\times 10^{-5}$, and an average two-qubit gate infidelity of $1.84(5)\times 10^{-3}$. The system-level performance of the quantum processor is assessed with mirror benchmarking, linear cross-entropy benchmarking, a quantum volume measurement of $\mathrm{QV}=2^{16}$, and the creation of 32-qubit entanglement in a GHZ state. We also tested application benchmarks including Hamiltonian simulation, QAOA, error correction on a repetition code, and dynamics simulations using qubit reuse. We also discuss future upgrades to the new system aimed at adding more qubits and capabilities.

quant-ph↗

arXiv2023

On $\mathbb{N}$-graded vertex algebras associated with cyclic Leibniz algebras with small dimensions

The main goals for this paper is i) to study of an algebraic structure of $\mathbb{N}$-graded vertex algebras $V_B$ associated to vertex $A$-algebroids $B$ when $B$ are cyclic non-Lie left Leibniz algebras, and ii) to explore relations between the vertex algebras $V_B$ and the rank one Heisenberg vertex operator algebra. To achieve these goals, we first classify vertex $A$-algebroids $B$ associated to given cyclic non-Lie left Leibniz algebras $B$. Next, we use the constructed vertex $A$-algebroids $B$ to create a family of indecomposable non-simple vertex algebras $V_B$. Finally, we use the algebraic structure of the unital commutative associative algebras $A$ that we found to study relations between a certain type of the vertex algebras $V_B$ and the vertex operator algebra associated to a rank one Heisenberg algebra.

math.QA↗

arXiv2022

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype.

physics.comp-ph↗

arXiv2022

Measurement of neutral current single $π^0$ production on argon with the MicroBooNE detector

We report the first measurement of $π^0$ 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 $π^0$ events are reported for two exclusive event topologies without charged pions. Those include a topology with two photons from the decay of the $π^0$ 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$π^0$, exclusive NC$π^0$+1p, and exclusive NC$π^0$+0p processes, respectively.

hep-ex↗

arXiv2022

Search for long-lived heavy neutral leptons and Higgs portal scalars decaying in the MicroBooNE detector

We present a search for long-lived Higgs portal scalars (HPS) and heavy neutral leptons (HNL) decaying in the MicroBooNE liquid-argon time projection chamber. The measurement is performed using data collected synchronously with the NuMI neutrino beam from Fermilab's Main Injector with a total exposure corresponding to $7.01 \times 10^{20}$ protons on target. We set upper limits at the $90\%$ confidence level on the mixing parameter $\lvert U_{μ4}\rvert^2$ ranging from $\lvert U_{μ4}\rvert^2<12.9\times 10^{-8}$ for Majorana HNLs with a mass of $m_{\rm HNL}=246$ MeV to $\lvert U_{μ4}\rvert^2<0.92 \times 10^{-8}$ for $m_{\rm HNL}=385$ MeV, assuming $\lvert U_{e 4}\rvert^2 = \lvert U_{τ4}\rvert^2 = 0$ and HNL decays into $μ^\pmπ^\mp$ pairs. These limits on $\lvert U_{μ4}\rvert^2$ represent an order of magnitude improvement in sensitivity compared to the previous MicroBooNE result. We also constrain the scalar-Higgs mixing angle $θ$ by searching for HPS decays into $μ^+μ^-$ final states, excluding a contour in the parameter space with lower bounds of $θ^2<31.3 \times 10^{-9}$ for $m_{\rm HPS}=212$ GeV and $θ^2<1.09 \times 10^{-9}$ for $m_{\rm HPS}=275$ GeV. These are the first constraints on the scalar-Higgs mixing angle $θ$ from a dedicated experimental search in this mass range.

hep-ex↗

arXiv2022

Observation of Radon Mitigation in MicroBooNE by a Liquid Argon Filtration System

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 99.999% or 97%, 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.

physics.ins-det↗

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.

hep-ex↗

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.

physics.ins-det↗

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

hep-ex↗

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

hep-ex↗

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.

hep-ex↗

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

physics.ins-det↗

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.

hep-ex↗

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.

hep-ex↗

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.

hep-ex↗