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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Measurement of Charged-Current $ν_μ$ and $ν_e$ Interactions Towards a Search for Low-Energy $ν_Ε$ Excess Using Wire-Cell In Microboone

This technote summarizes the existing work in searching for $ν_e$ low-energy excess (eLEE) in MicroBooNE Booster Neutrino Beam (BNB) data stream based on the Wire-Cell event reconstruction paradigm. The charged-current $ν_μ$ and $ν_e$ events are selected from the 5.3e19 POT open data from the BNB beam, 6.37e20 POT far sideband from the BNB beam data, and 2.10e20 POT data from Neutrinos at the Main Injector (NuMI) beam. The charged-current $ν_e$ selection results from the BNB data that are sensitive to the eLEE search are not included. Various comparisons between data and Monte Carlo predictions are performed to validate the overall model and demonstrate the power of the analysis techniques. Physics sensitivities in terms of the exclusion and the discovery potential are presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Measurement of charged-current $\nu_{\mu}$ and $\nu_e$ interactions with wire-cell in MicroBooNE towards a search for low-energy $\nu_e$ excess

This technote summarizes the existing work in searching for $\nu_e$ low-energy excess (eLEE) in MicroBooNE Booster Neutrino Beam (BNB) data stream based on the Wire-Cell event reconstruction paradigm. The charged-current $\nu_{\mu}$ and $\nu_e$ events are selected from the 5.3e19 POT data from the BNB beam and 2.06e20 POT data from Neutrinos at the Main Injector (NuMI) beam. The charged-current $\nu_e$ selection results from the BNB data that are sensitive to the eLEE search are not included. Various comparisons between data and Monte Carlo predictions are performed to validate the overall model and demonstrate the power of the analysis techniques.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Real-Time Artificial Intelligence for Particle Reconstruction and Higgs Physics

With the discovery of the Higgs boson at the CERN LHC, the world's highest-energy particle accelerator complex, scientists have acquired an important tool to study the fundamental building blocks of the universe. Precision measurements of Higgs bosons produced with large momentum allow for unique insights into the structure of the interactions of the Higgs boson with other particles that may shed light on physics beyond the standard model. While experimentally challenging, exploring such interactions with novel artificial intelligence (AI) methods can advance our understanding of the Higgs sector, including the Higgs boson's self-interaction. Moreover, the LHC is undergoing a major upgrade to further increase its particle collision rate and thereby operate for an additional decade. The experimental detectors at the upgraded facility must process at least a factor of ten more data at rates of hundreds of terabytes per second all under challenging conditions. New AI techniques are required to reconstruct and select, or trigger on, the most physics-sensitive events in real-time to handle the resulting avalanche of data. The proposed research will achieve the goals of the LHC program at the CMS experiment by developing a sub-microsecond event reconstruction system using real-time AI algorithms that employ field-programmable gate array technologies. By harnessing sophisticated AI techniques, this research focuses on measuring the production of Higgs bosons at large momentum while enhancing particle reconstruction methods in the trigger and beyond. Overall, the proposed research has broader implications for the use of AI in resource-constrained, low-latency embedded applications across all fields of science.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Cosmic antihelium-3 nuclei sensitivity of the GAPS experiment

The General Antiparticle Spectrometer (GAPS) is an Antarctic balloon experiment designed for low-energy (0.1-0.3 GeV/n) cosmic antinuclei as signatures of dark matter annihilation or decay. GAPS is optimized to detect low-energy antideuterons, as well as to provide unprecedented sensitivity to low-energy antiprotons and antihelium nuclei. The novel GAPS antiparticle detection technique, based on the formation, decay, and annihilation of exotic atoms, provides greater identification power for these low-energy antinuclei than previous magnetic spectrometer experiments. This work reports the sensitivity of GAPS to detect antihelium-3 nuclei, based on full instrument simulation, event reconstruction, and realistic atmospheric influence simulations. The report of antihelium nuclei candidate events by AMS-02 has generated considerable interest in antihelium nuclei as probes of dark matter and other beyond the Standard Model theories. GAPS is in a unique position to detect or set upper limits on the cosmic antihelium nuclei flux in an energy range that is essentially free of astrophysical background. In three 35-day long-duration balloon flights, GAPS will be sensitive to an antihelium flux on the level of $1.3^{+4.5}_{-1.2} $ ∙ 10 -6 m -2 sr -1 s -1 (GeV/n) -1 (95% confidence level) in the energy range of 0.11-0.3 GeV/n, opening a new window on rare cosmic physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

EJFAT Scientific Perspective

Presented new computing model to the test by deploying the EJFAT system alongside a data-stream processing framework running the production-level CLAS12 event reconstruction application. In this experiment, a continuous stream of CLAS12 Level-1 identified events was processed in real-time using the EJFAT load balancer, distributing the workload across 90 computing nodes located across the U.S. This marks the first-ever large-scale, real-time distributed data stream processing experiment, demonstrating that scientific data-streaming pipelines can efficiently scale across four dimensions, thanks to EJFAT’s advanced hardware and software capabilities.

Gyurjyan, Vardan [Thomas Jefferson National Accele↗
DOE OSTI2020

The CLAS12 Forward Time-of-Flight system

The Forward Time-of-Flight system for the large-acceptance CLAS12 spectrometer in Hall B at the Thomas Jefferson National Accelerator Facility is described. The system is positioned at distances in the range from 6.2 m to 7.2 m from the beam–target interaction point and spans laboratory polar angles from 5 deg → 45 deg and nearly the full azimuth. The system consists of 540 individual scintillation counters with double-ended readout that range in length from 17 cm to 426 cm of discrete widths of 6 cm, 15 cm, and 22 cm, and of discrete thicknesses of 5 cm and 6 cm. We see the effective counter time resolution for passing charged particles varies from 50 ps for the shortest counters at small angles to 200 ps for the longest counters at large angles. The detectors are part of the forward-angle particle identification system for CLAS12 during offline event reconstruction and are a component of the online data acquisition trigger to select final state event topologies with forward-going charged particles.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Neutrino interaction vertex reconstruction in DUNE with Pandora deep learning

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Cosmic Background Rejection of the ICARUS experiment at Fermilab

The Short Baseline Neutrino program at Fermilab aims to explore significant regions of parameter space, applicable to sterile neutrinos at the eV mass scale, as suggested by existing experimental anomalies. To this purpose it exploits Liquid Argon Time Projection Chamber detectors located along the Booster Neutrino Beamline to measure both νe appearance and νµ disappearance: the Short Baseline Neutrino Detector and the ICARUS-T600 detector at 110 and 600 m from the neutrino source, respectively. The ICARUS T-600 Far Detector, located at shallow depth, is surrounded by a Cosmic Ray Tagger system to mitigate the cosmic ray background. On average ~ 11 muon tracks are expected to cross the detector during the ~ 1 ms drift time. The cosmic ray tagger is composed of plastic scintillator bars, ensuring near 4π coverage of the detector aiming at tagging cosmic muons and thus reject 𝛾s produced by muon interactions in the surrounding materials that can generate an electromagnetic showers mimicking a νe signal. The system allows one to disentangle cosmic rays from particles originated in a neutrino interaction inside the detector by measuring their position and crossing time. A synchronization of the cosmic ray tagger with the ICARUS photon detection system with a nanosecond accuracy allows one to reject cosmic particles recorded during the beam spill and thus select an enriched sample of neutrino triggered events ahead of the event reconstruction. An overview of the cosmic ray tagger system as well as its role in the neutrino events identification and cosmic background rejection will be presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Convolutional Neural Networks for the CHIPS Neutrino Detector R&D Project.

The CHerenkov detectors In mine PitS (Chips) neutrino detector R&D project aims to develop novel strategies and technologies for very large yet ‘cheap as chips’ water Cherenkov neutrino detectors. Via deployment in a body of water, use of commercially available components, and instrumentation coverage optimisation for the study of exclusively accelerator beam neutrinos, Chips will enable megaton scale detectors to become a reality at the cost of $200k-$300k per kt of sensitive mass. During the summer of 2019 a prototype Chips detector, Chips-5, was deployed into the Wentworth 2W disused mine pit in northern Minnesota, 7 mrad off the NuMI beam axis. A novel data acquisition system was introduced using cheap single-board computers and open-source software. This work presents a novel approach to water Cherenkov neutrino detector event reconstruction and classification. Three forms of a Convolutional Neural Network, a type of deep learning algorithm, have been trained to reject cosmic muon events, classify beam events, and estimate neutrino energies, all using only the raw detector event as input. When evaluated on the expected distribution of Chips-5 events, this new approach is shown to be robust and explainable as well as providing a significant performance increase over the standard likelihood-based reconstruction and simple neural network classification. Promisingly, the performance presented here is comparable to the more complex (and expensive) neutrino oscillation experiments within the field.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Full Detector Simulation with Unprecedented Background Occupancy at a Muon Collider

In recent years, a Muon collider has attracted a lot of interest in the high-energy physics community, thanks to its ability of achieving clean interaction signatures at multi-TeV collision energies in the most cost-effective way. Estimation of the physics potential of such an experiment must take into account the impact of beam-induced background on the detector performance, which has to be carefully evaluated using full detector simulation. Tracing of all the background particles entering the detector region in a single bunch crossing is out of reach for any realistic computing facility due to the unprecedented number of such particles. To make it feasible a number of optimisations have been applied to the detector simulation workflow. This contribution presents an overview of the main characteristics of the beam-induced background at a Muon collider, the detector technologies considered for the experiment and how they are taken into account to strongly reduce the number of irrelevant computations performed during the detector simulation. Special attention is dedicated to the optimisation of track reconstruction with the conformal tracking algorithm in this high-occupancy environment, which is the most computationally demanding part of event reconstruction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Calibration and Timing Performance of the Light Detection System in the ICARUS Detector

ICARUS is the largest Liquid Argon Time Projection Chamber (LArTPC) in operation and serves as the Far Detector of the Short Baseline Neutrino (SBN) program at Fermilab. It aims to investigate the possible existence of sterile neutrinos with $\Delta m^2 \approx \SI{1}{eV^2}$ using the Booster Neutrino Beam (BNB) and explore physics beyond the Standard Model with the Neutrinos at the Main Injector (NuMI) beam. The ICARUS light detection system, comprising 360 TPB-coated large-area Photo-Multiplier Tubes (PMTs), is crucial for triggering and event reconstruction. Due to its shallow installation, the detector is exposed to a high flux of cosmic rays, necessitating precise timing to reject background events and align neutrino interactions with the beam time profile. This talk will detail the timing inter-calibration procedures for the ICARUS light detection system, which achieve sub-nanosecond resolution. Additionally, the performance of the system in reconstructing the timing of neutrino interactions from the BNB and NuMI beams will be discussed. The results highlight the effectiveness of the ICARUS light detection system in enhancing the detector's capability for precise and reliable neutrino selection.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Calibration and Timing Performance of the Light Detection System in the ICARUS Detector

ICARUS is the largest Liquid Argon Time Projection Chamber (LArTPC) in operation and serves as the Far Detector of the Short Baseline Neutrino (SBN) program at Fermilab. It aims to investigate the possible existence of sterile neutrinos with $\Delta m^2 \approx 1 \,\mathrm{eV^2}$ using the Booster Neutrino Beam (BNB) and explore physics beyond the Standard Model with the Neutrinos at the Main Injector (NuMI) beam. The ICARUS light detection system, comprising 360 TPB-coated large-area Photo-Multiplier Tubes (PMTs), is crucial for triggering and event reconstruction. Due to its shallow installation, the detector is exposed to a high flux of cosmic rays, necessitating precise timing to reject background events and align neutrino interactions with the beam time profile. This talk will detail the timing inter-calibration procedures for the ICARUS light detection system, which achieve sub-nanosecond resolution. Additionally, the performance of the system in reconstructing the timing of neutrino interactions from the BNB and NuMI beams will be discussed. The results highlight the effectiveness of the ICARUS light detection system in enhancing the detector's capability for precise and reliable neutrino selection.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Understanding hadronic interactions using advanced reconstruction techniques

ProtoDUNE-SP served as the prototype of the future Deep Underground Neutrino Experiment (DUNE). With a total liquid argon mass of 0.77 kt, it stood as the most extensive monolithic single-phase Liquid Argon Time Projection Chamber (LArTPC) ever constructed. Strategically located and operated at CERN, ProtoDUNE-SP benefited from a specialized charged-particle test beam, offering a momentum range of 0.3 - 7 GeV/c. This setup provided a unique opportunity to conduct in-depth studies on hadronic interactions within argon. Hadronic interactions play a fundamental role in neutrino physics, particularly in the intricacies of neutrino detection. We depend on analyzing the outcomes of these interactions to accurately determine both the flavor and energy of the interacting neutrinos. Pions, as one of the primary byproducts of neutrino interactions, are crucial for understanding the overall dynamics and kinematics of these processes. Accurately characterizing and understanding the interaction of pions with argon can significantly enhance the precision of neutrino simulations and measurements.Being a Liquid Argon Time Projection Chamber (LArTPC), ProtoDUNE-SP is distinguished by its ability to produce high-definition images of charged particles as they traverse through the detector's active volume. Yet, accurately reconstructing these particle interactions and determining their kinematic attributes remains a challenging task. The successes of deep learning in diverse domains, especially in image recognition, provide a promising approach for addressing this challenge in ProtoDUNE. This thesis discusses a measurement of the Piplus - argon inelastic cross section in the energy range of 2400 to 3000 MeV, using data taken by protoDUNE during the fall of 2018. Additionally, panoptic segmentation, a machine learning technique, is introduced and validated through its application in the reconstruction of the neutral pion rest mass. The aim is to showcase how these advanced methods can enhance the quality of event reconstruction.

Sarasty Segura, Carlos Eduardo↗
DOE OSTI2020

Speeding up particle track reconstruction using a parallel Kalman filter algorithm

One of the most computationally difficult problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is determining the trajectory of charged particles during event reconstruction. Algorithms used at the LHC today rely on Kalman filtering, which builds physical trajectories incrementally while incorporating material effects and error estimation. Recognizing the need for faster computational throughput, we have adapted Kalman-filter-based methods for highly parallel, many-core SIMD architectures that are now prevalent in high-performance hardware. In this paper, we discuss the design and performance of the improved tracking algorithm, referred to as mkFit. A key piece of the algorithm is the Matriplex library, containing dedicated code to optimally vectorize operations on small matrices. The physics performance of the mkFit algorithm is comparable to the nominal CMS tracking algorithm when reconstructing tracks from simulated proton-proton collisions within the CMS detector. We study the scaling of the algorithm as a function of the parallel resources utilized and find large speedups both from vectorization and multi-threading. mkFit achieves a speedup of a factor of 6 compared to the nominal algorithm when run in a single-threaded application within the CMS software framework.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Emerging Jets Search, Triton Server Deployment, and Track Quality Development: Machine Learning Applications in High Energy Physics

Machine learning is becoming prevalent in high energy physics, with numerous applications in physics analyses and event reconstruction showing great improvements compared to traditional computing methods. This thesis studies three projects which each propose new avenues for machine learning applications within the high energy physics CMS experiment located at CERN. In the first project, a search for a dark matter signal called “emerging jets” is performed, using graph neural networks to greatly increase sensitivity to the signal’s signature within the data. The result of this dark matter search sets the most stringent exclusion limits to date on theoretical emerging jet models. Motivated by inefficiencies encountered when processing the emerging jet graph neural network at Fermi National Accelerator Laboratory’s computing centers, the second project re-optimizes the computing centers for machine learning inference. This re-optimization uses NVIDIA Triton Inference Servers to process users’ analysis code heterogeneously, therefore achieving high processing throughput and decreasing user time-to-insight. The last project focuses on an upgrade to the CMS experiment’s real-time event selection system which improves physics object reconstruction under harsh processing conditions. A boosted decision tree is used to quickly and efficiently quantify a reconstructed particle’s “track quality” in order to remove particle tracks reconstructed erroneously. In summary, this thesis will not only present examples of how high energy physics can greatly benefit by leveraging machine learning techniques for physics analysis and reconstruction, but will also provide guidance on how the field can prepare for the inevitable increase in machine learning applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Simulated Detector Performance at the Muon Collider

In this paper we report on the current status of studies on the expected performance for a detector designed to operate in a muon collider environment. Beam-induced backgrounds (BIB) represent the main challenge in the design of the detector and the event reconstruction algorithms. The current detector design aims to show that satisfactory performance can be achieved, while further optimizations are expected to significantly improve the overall performance. We present the characterization of the expected beam-induced background, describe the detector design and software used for detailed event simulations taking into account BIB effects. The expected performance of charged-particle reconstruction, jets, electrons, photons and muons is discussed, including an initial study on heavy-flavor jet tagging. A simple method to measure the delivered luminosity is also described. Overall, the proposed design and reconstruction algorithms can successfully reconstruct the high transverse-momentum objects needed to carry out a broad physics program.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Exploring Dark Sector Physics in MicroBooNE with e+e- Final States

Neutrinos are the second most abundant massive particle in the universe, but because of their small mass and weak interactions with matter, they are still not well understood. Due to their pervasive nature and unique properties, neutrinos make great probes to explore fundamental questions about the universe such as what is the composition of dark matter? The MiniBooNE experiment, designed to study neutrino oscillations, detected what has become a longstanding anomaly – an excess of low energy electromagnetic events from accelerator neutrino interactions. A possible explanation of the anomaly comes from neutrino interactions with nuclei that create heavier, sterile neutrinos that will decay quickly into electron-positron pairs. Complementing this is the MicroBooNE experiment, a liquid argon time projection chamber, that can reconstruct events well in both two and three dimensions and can distinguish electrons from photons. This reconstruction ability enables us to select dark sector neutrino interactions from other possible interactions using machine learning techniques such as graph neural networks (GNNs) which is the primary focus of this project.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Reconstruction of Charged Particle Tracks in Realistic Detector Geometry Using a Vectorized and Parallelized Kalman Filter Algorithm

One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is finding and fitting particle tracks during event reconstruction. Algorithms used at the LHC today rely on Kalman filtering, which builds physical trajectories incrementally while incorporating material e ects and error estimation. Recognizing the need for faster computational throughput, we have adapted Kalman-filterbased methods for highly parallel, many-core SIMD and SIMT architectures that are now prevalent in high-performance hardware. Previously we observed significant parallel speedups, with physics performance comparable to CMS standard tracking, on Intel Xeon, Intel Xeon Phi, and (to a limited extent) NVIDIA GPUs. While early tests were based on artificial events occurring inside an idealized barrel detector, we showed subsequently that our mkFit software builds tracks successfully from complex simulated events (including detector pileup) occurring inside a geometrically accurate representation of the CMS-2017 tracker. Here, we report on advances in both the computational and physics performance of mkFit, as well as progress toward integration with CMS production software. Recently we have improved the overall eciency of the algorithm by preserving short track candidates at a relatively early stage rather than attempting to extend them over many layers. Moreover, mkFit formerly produced an excess of duplicate tracks; these are now explicitly removed in an additional processing step. We demonstrate that with these enhancements, mkFit becomes a suitable choice for the first iteration of CMS tracking, and eventually for later iterations as well. We plan to test this capability in the CMS High Level Trigger during Run 3 of the LHC, with an ultimate goal of using it in both the CMS HLT and oine reconstruction for the HL-LHC CMS tracker.

Cerati, Giuseppe↗