arXiv subjects

V Hewes

Publications and source records attributed to V Hewes.

At least 37 records · Page 2Linked to original sources

arXiv2021

Search for active-sterile antineutrino mixing using neutral-current interactions with the NOvA experiment

This Letter reports results from the first long-baseline search for sterile antineutrinos mixing in an accelerator-based antineutrino-dominated beam. The rate of neutral-current interactions in the two NOvA detectors, at distances of 1 km and 810 km from the beam source, is analyzed using an exposure of $12.51\times10^{20}$ protons-on-target from the NuMI beam at Fermilab running in antineutrino mode. A total of $121$ of neutral-current candidates are observed at the Far Detector, compared to a prediction of $122\pm11$(stat.)$\pm15$(syst.) assuming mixing between three active flavors. No evidence for $\barν_μ\rightarrow\barν_{s}$ oscillation is observed. Interpreting this result within a 3+1 model, constraints are placed on the mixing angles $θ_{24} < 25^{\circ}$ and $θ_{34} < 32^{\circ}$ at the 90% C.L. for $0.05$eV$^{2} \leq Δm^{2}_{41} \leq 0.5$eV$^{2}$, the range of mass splittings that produces no significant oscillations at the Near Detector. These are the first 3+1 confidence limits set using long-baseline accelerator antineutrinos.

hep-ex↗

arXiv2021

Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking

The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX's tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.

physics.data-an↗

arXiv2021

Extended search for supernova-like neutrinos in NOvA coincident with LIGO/Virgo detections

A search is performed for supernova-like neutrino interactions coincident with 76 gravitational wave events detected by the LIGO/Virgo Collaboration. For 40 of these events, full readout of the time around the gravitational wave is available from the NOvA Far Detector. For these events, we set limits on the fluence of the sum of all neutrino flavors of $F < 7(4)\times 10^{10}\mathrm{cm}^{-2}$ at 90% C.L. assuming energy and time distributions corresponding to the Garching supernova models with masses 9.6(27)$\mathrm{M}_\odot$. Under the hypothesis that any given gravitational wave event was caused by a supernova, this corresponds to a distance of $r > 29(50)$kpc at 90% C.L. Weaker limits are set for other gravitational wave events with partial Far Detector data and/or Near Detector data.

hep-ex↗

arXiv2021

Seasonal Variation of Multiple-Muon Cosmic Ray Air Showers Observed in the NOvA Detector on the Surface

We report the rate of cosmic ray air showers with multiplicities exceeding 15 muon tracks recorded in the NOvA Far Detector between May 2016 and May 2018. The detector is located on the surface under an overburden of 3.6 meters water equivalent. We observe a seasonal dependence in the rate of multiple-muon showers, which varies in magnitude with multiplicity and zenith angle. During this period, the effective atmospheric temperature and surface pressure ranged between 210 K to 230 K and 940mbar to 990mbar, respectively; the shower rates are anti-correlated with the variation in the effective temperature. The variations are about 30% larger for the highest multiplicities than the lowest multiplicities and 20% larger for showers near the horizon than vertical showers.

hep-ex↗

arXiv2021

Search for multi-messenger signals in NOvA coincident with LIGO/Virgo detections

Using the NOvA neutrino detectors, a broad search has been performed for any signal coincident with 28 gravitational wave events detected by the LIGO/Virgo Collaboration between September 2015 and July 2019. For all of these events, NOvA is sensitive to possible arrival of neutrinos and cosmic rays of GeV and higher energies. For five (seven) events in the NOvA Far (Near) Detector, timely public alerts from the LIGO/Virgo Collaboration allowed recording of MeV-scale events. No signal candidates were found.

hep-ex↗

arXiv2021

Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers

This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar reconstruction tasks in the LHC. In this paper, a multihead attention message passing network is used to classify the relationship between detector hits by labelling graph edges, determining whether hits were produced by the same underlying particle, and if so, the particle type. The trained model is 84% accurate overall, and performs best on the EM shower and muon track classes. The model's strengths and weaknesses are discussed, and plans for developing this technique further are summarised.

hep-ex↗

arXiv2021

Search for Slow Magnetic Monopoles with the NOvA Detector on the Surface

We report a search for a magnetic monopole component of the cosmic-ray flux in a 95-day exposure of the NOvA experiment's Far Detector, a 14 kt segmented liquid scintillator detector designed primarily to observe GeV-scale electron neutrinos. No events consistent with monopoles were observed, setting an upper limit on the flux of $2\times 10^{-14} \mathrm{cm^{-2}s^{-1}sr^{-1}}$ at 90% C.L. for monopole speed $6\times 10^{-4} < β< 5\times 10^{-3}$ and mass greater than $5\times 10^{8}$ GeV. Because of NOvA's small overburden of 3 meters-water equivalent, this constraint covers a previously unexplored low-mass region.

hep-ex↗

arXiv2020

Adjusting Neutrino Interaction Models and Evaluating Uncertainties using NOvA Near Detector Data

The two-detector design of the NOvA neutrino oscillation experiment, in which two functionally identical detectors are exposed to an intense neutrino beam, aids in canceling leading order effects of cross-section uncertainties. However, limited knowledge of neutrino interaction cross sections still gives rise to some of the largest systematic uncertainties in current oscillation measurements. We show contemporary models of neutrino interactions to be discrepant with data from NOvA, consistent with discrepancies seen in other experiments. Adjustments to neutrino interaction models in GENIE that improve agreement with our data are presented. We also describe systematic uncertainties on these models, including uncertainties on multi-nucleon interactions from a newly developed procedure using NOvA near detector data.

hep-ex↗

arXiv2020

Supernova neutrino detection in NOvA

The NOvA long-baseline neutrino experiment uses a pair of large, segmented, liquid-scintillator calorimeters to study neutrino oscillations, using GeV-scale neutrinos from the Fermilab NuMI beam. These detectors are also sensitive to the flux of neutrinos which are emitted during a core-collapse supernova through inverse beta decay interactions on carbon at energies of $\mathcal{O}(10~\text{MeV})$. This signature provides a means to study the dominant mode of energy release for a core-collapse supernova occurring in our galaxy. We describe the data-driven software trigger system developed and employed by the NOvA experiment to identify and record neutrino data from nearby galactic supernovae. This technique has been used by NOvA to self-trigger on potential core-collapse supernovae in our galaxy, with an estimated sensitivity reaching out to 10~kpc distance while achieving a detection efficiency of 23\% to 49\% for supernovae from progenitor stars with masses of 9.6M$_\odot$ to 27M$_\odot$, respectively.

physics.ins-det↗

arXiv2020

Track Seeding and Labelling with Embedded-space Graph Neural Networks

To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detector hits corresponding to nodes) with candidate line segments between the hits (corresponding to edges). Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables. Previously, message-passing GNNs have shown success in predicting doublet likelihood, and we here report updates on the state-of-the-art architectures for this task. In addition, the Exa.TrkX project has investigated innovations in both graph construction, and embedded representations, in an effort to achieve fully learned end-to-end track finding. Hence, we present a suite of extensions to the original model, with encouraging results for hitgraph classification. In addition, we explore increased performance by constructing graphs from learned representations which contain non-linear metric structure, allowing for efficient clustering and neighborhood queries of data points. We demonstrate how this framework fits in with both traditional clustering pipelines, and GNN approaches. The embedded graphs feed into high-accuracy doublet and triplet classifiers, or can be used as an end-to-end track classifier by clustering in an embedded space. A set of post-processing methods improve performance with knowledge of the detector physics. Finally, we present numerical results on the TrackML particle tracking challenge dataset, where our framework shows favorable results in both seeding and track finding.

physics.ins-det↗

arXiv2020

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.

physics.ins-det↗

arXiv2019

Observation of seasonal variation of atmospheric multiple-muon events in the NOvA Near Detector

Using two years of data from the NOvA Near Detector at Fermilab, we report a seasonal variation of cosmic ray induced multiple-muon event rates which has an opposite phase to the seasonal variation in the atmospheric temperature. The strength of the seasonal multipl$ increase as a function of the muon multiplicity. However, no significant dependence of the strength of the seasonal variation of the multiple-muon variation is seen as a function of the muon zenith angle, or the spatial or angular separation between the correlated muons.

physics.ins-det↗

arXiv2019

First measurement of neutrino oscillation parameters using neutrinos and antineutrinos by NOvA

The NOvA experiment has made a $4.4σ$-significant observation of $\barν_{e}$ appearance in a 2 GeV $\barν_μ$ beam at a distance of 810 km. Using $12.33\times10^{20}$ protons on target delivered to the Fermilab NuMI neutrino beamline, the experiment recorded 27 $\barν_μ \rightarrow \barν_{e}$ candidates with a background of 10.3 and 102 $\barν_μ \rightarrow \barν_μ$ candidates. This new antineutrino data is combined with neutrino data to measure the oscillation parameters $|Δm^2_{32}| = 2.48^{+0.11}_{-0.06}\times10^{-3}$ eV$^2/c^4$, $\sin^2 θ_{23} = 0.56^{+0.04}_{-0.03}$ in the normal neutrino mass hierarchy and upper octant and excludes most values near $δ_{\rm CP}=π/2$ for the inverted mass hierarchy by more than 3$σ$. The data favor the normal neutrino mass hierarchy by 1.9$σ$ and $θ_{23}$ values in the upper octant by 1.6$σ$.

hep-ex↗

arXiv2019

Comparison of νμ-Ar multiplicity distributions observed by MicroBooNE to GENIE model predictions

We measure a large set of observables in inclusive charged current muon neutrino scattering on argon with the MicroBooNE liquid argon time projection chamber operating at Fermilab. We evaluate three neutrino interaction models based on the widely used GENIE event generator using these observables. The measurement uses a data set consisting of neutrino interactions with a final state muon candidate fully contained within the MicroBooNE detector. These data were collected in 2016 with the Fermilab Booster Neutrino Beam, which has an average neutrino energy of 800 MeV, using an exposure corresponding to 5E19 protons-on-target. The analysis employs fully automatic event selection and charged particle track reconstruction and uses a data-driven technique to separate neutrino interactions from cosmic ray background events. We find that GENIE models consistently describe the shapes of a large number of kinematic distributions for fixed observed multiplicity.

hep-ex↗

arXiv2019

Design and construction of the MicroBooNE Cosmic Ray Tagger system

The MicroBooNE detector utilizes a liquid argon time projection chamber (LArTPC) with an 85 t active mass to study neutrino interactions along the Booster Neutrino Beam (BNB) at Fermilab. With a deployment location near ground level, the detector records many cosmic muon tracks in each beam-related detector trigger that can be misidentified as signals of interest. To reduce these cosmogenic backgrounds, we have designed and constructed a TPC-external Cosmic Ray Tagger (CRT). This sub-system was developed by the Laboratory for High Energy Physics (LHEP), Albert Einstein center for fundamental physics, University of Bern. The system utilizes plastic scintillation modules to provide precise time and position information for TPC-traversing particles. Successful matching of TPC tracks and CRT data will allow us to reduce cosmogenic background and better characterize the light collection system and LArTPC data using cosmic muons. In this paper we describe the design and installation of the MicroBooNE CRT system and provide an overview of a series of tests done to verify the proper operation of the system and its components during installation, commissioning, and physics data-taking.

physics.ins-det↗

arXiv2019

Rejecting cosmic background for exclusive neutrino interaction studies with Liquid Argon TPCs; a case study with the MicroBooNE detector

Cosmic ray (CR) interactions can be a challenging source of background for neutrino oscillation and cross-section measurements in surface detectors. We present methods for CR rejection in measurements of charged-current quasielastic-like (CCQE-like) neutrino interactions, with a muon and a proton in the final state, measured using liquid argon time projection chambers (LArTPCs). Using a sample of cosmic data collected with the MicroBooNE detector, mixed with simulated neutrino scattering events, a set of event selection criteria is developed that produces an event sample with minimal contribution from CR background. Depending on the selection criteria used a purity between 50% and 80% can be achieved with a signal selection efficiency between 50% and 25%, with higher purity coming at the expense of lower efficiency. While using a specific dataset from the MicroBooNE detector and selection criteria values optimized for CCQE-like events, the concepts presented here are generic and can be adapted for various studies of exclusive νμ interactions in LArTPCs.

physics.ins-det↗

arXiv2018

First Measurement of $ν_μ$ Charged-Current $π^{0}$ Production on Argon with a LArTPC

We report the first measurement of the flux-integrated cross section of $ν_μ$ charged-current single $π^{0}$ production on argon. This measurement is performed with the MicroBooNE detector, an 85 ton active mass liquid argon time projection chamber exposed to the Booster Neutrino Beam at Fermilab. This result on argon is compared to past measurements on lighter nuclei to investigate the scaling assumptions used in models of the production and transport of pions in neutrino-nucleus scattering. The techniques used are an important demonstration of the successful reconstruction and analysis of neutrino interactions producing electromagnetic final states using a liquid argon time projection chamber operating at the earth's surface.

hep-ex↗

arXiv2018

A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber

We have developed a convolutional neural network (CNN) that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a $ν_μ$ charged current neutral pion data samples.

hep-ex↗