Search NASA⌕ Search

SEARCH · Search NASA

Results for “particle reconstruction”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4

A substrate-induced gating mechanism is conserved among Gram-positive IgA1 metalloproteases

The mucosal adaptive immune response is dependent on the production of IgA antibodies and particularly IgA1, yet opportunistic bacteria have evolved mechanisms to specifically block this response by producing IgA1 proteases (IgA1Ps). Our lab was the first to describe the structures of a metal-dependent IgA1P (metallo-IgA1P) produced from Gram-positive Streptococcus pneumoniae both in the absence and presence of its IgA1 substrate through cryo-EM single particle reconstructions. This prior study revealed an active-site gating mechanism reliant on substrate-induced conformational changes to the enzyme that begged the question of whether such a mechanism is conserved among the wider Gram-positive metallo-IgA1P subfamily of virulence factors. Here, we used cryo-EM to characterize the metallo-IgA1P of a more distantly related family member from Gemella haemolysans, an emerging opportunistic pathogen implicated in meningitis, endocarditis, and more recently bacteremia in the elderly. While the substrate-free structures of these two metallo-IgA1Ps exhibit differences in the relative starting positions of the domain responsible for gating substrate, the enzymes have similar domain orientations when bound to IgA1. Together with biochemical studies that indicate these metallo-IgA1Ps have similar binding affinities and activities, these data indicate that metallo-IgA1P binding requires the specific IgA1 substrate to open the enzymes for access to their active site and thus, largely conform to an “induced fit” model.

59 BASIC BIOLOGICAL SCIENCES↗

Performance of the CMS Level-1 trigger in proton-proton collisions at $\sqrt{s} =$ 13 TeV

At the start of Run 2 in 2015, the LHC delivered proton-proton collisions at a center-of-mass energy of 13\TeV. During Run 2 (years 2015–2018) the LHC eventually reached a luminosity of 2.1× 1034 cm-2s-1, almost three times that reached during Run 1 (2009–2013) and a factor of two larger than the LHC design value, leading to events with up to a mean of about 50 simultaneous inelastic proton-proton collisions per bunch crossing (pileup). The CMS Level-1 trigger was upgraded prior to 2016 to improve the selection of physics events in the challenging conditions posed by the second run of the LHC. This paper describes the performance of the CMS Level-1 trigger upgrade during the data taking period of 2016–2018. The upgraded trigger implements pattern recognition and boosted decision tree regression techniques for muon reconstruction, includes pileup subtraction for jets and energy sums, and incorporates pileup-dependent isolation requirements for electrons and tau leptons. In addition, the new trigger calculates high-level quantities such as the invariant mass of pairs of reconstructed particles. The upgrade reduces the trigger rate from background processes and improves the trigger efficiency for a wide variety of physics signals.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Boosted decision tree reweighting of simulated neutrino interactions for O ( 1 ) GeV neutrino cross-section measurements

This paper illustrates a generic method for multidimensional reweighting of O ( 1 ) GeV neutrino interaction Monte Carlo samples. The reweighting is based on a boosted decision tree algorithm trained on high-dimensional space in detector final-state observables. This enables one generator’s events to be reweighted so that its reconstructed particle content and kinematics distributions, as well as detector efficiency, match those of a target model. The approach establishes an efficient way to reuse legacy Monte Carlo data, avoiding regeneration. As an example, we test its use in a measurement of transverse kinematic imbalance of the μ - and proton in charged-current quasielastic like ν μ events from the MINERvA experiment.

Lin, Z. [Rochester U.] (ORCID:0009000188903698)↗

Mesoscale Effects of Composition and Calendering in Lithium-Ion Battery Composite Electrodes

Macrohomogeneous battery models are widely used to predict battery performance, necessarily relying on effective electrode properties, such as specific surface area, tortuosity, and electrical conductivity. While these properties are typically estimated using ideal effective medium theories, in practice they exhibit highly non-ideal behaviors arising from their complex mesostructures. In this paper, we computationally reconstruct electrodes from X-ray computed tomography of 16 nickel–manganese–cobalt-oxide electrodes, manufactured using various material recipes and calendering pressures. Due to imaging limitations, a synthetic conductive binder domain (CBD) consisting of binder and conductive carbon is added to the reconstructions using a binder bridge algorithm. Reconstructed particle surface areas are significantly smaller than standard approximations predicted, as the majority of the particle surface area is covered by CBD, affecting electrochemical reaction availability. Finite element effective property simulations are performed on 320 large electrode subdomains to analyze trends and heterogeneity across the electrodes. Significant anisotropy of up to 27% in tortuosity and 47% in effective conductivity is observed. Electrical conductivity increases up to 7.5× with particle lithiation. We compare the results to traditional Bruggeman approximations and offer improved alternatives for use in cell-scale modeling, with Bruggeman exponents ranging from 1.62 to 1.72 rather than the theoretical value of 1.5. We also conclude that the CBD phase alone, rather than the entire solid phase, should be used to estimate effective electronic conductivity. This study provides insight into mesoscale transport phenomena and results in improved effective property approximations founded on realistic, image-based morphologies.

25 ENERGY STORAGE↗

Mixed Material Scintillator Systems for Neutron Detection

Scintillators are a fundamental technology that enables radiation detection. Their use has historically enabled the detection of incoming gamma radiation, with recent advances in plastic scintillator development enabling detection and discrimination of neutron and gamma radiation through pulse shape discrimination. However, scintillators have so far consisted of a single material, typically one primary dye and one secondary dye embedded in a plastic matrix. Based on the simulation studies described previously, mixed material scintillator systems with multiple secondary dyes arranged in alternating, periodic micro-sized regions opens the door for scintillators with a wide variety of different capability, such as directional sensing, positional reconstruction, particle identification, and pulse shape discrimination. However, fabrication of a mixed material scintillator system by conventional manufacturing methods is difficult to manufacture and scale, creating a barrier to their wide spread implementation. On the other hand, additive manufacturing additive manufacturing presents itself as the optimal method for fabricating mixed material scintillator systems based on its flexibility, ability to fabricate fine features, and potential for multimaterial fabrication.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Track vs Shower Discrimination in the Event Reconstruction of the ICARUS Experiment

The ICARUS collaboration has employed the 760-ton T600 liquid argon TPC detector in a successful three-year physics run at the underground LNGS laboratory, performing a sensitive search for LSND-like anomalous appearance in the CERN Neutrino to Gran Sasso beam, which contributed to the constraints on the allowed neutrino oscillation parameters to a narrow region around 1 eV. After a significant overhaul at CERN, the T600 detector has been installed at Fermilab. The detector commissioning phase lasted until June 2022, then ICARUS moved to data taking for neutrino oscillation physics collecting events from the Booster Neutrino Beam (BNB) and the Neutrinos at the Main Injector (NuMI) beam off-axis. The initial experiment goals are to either confirm or refute the claim by Neutrino-4 short-baseline reactor experiment, perform measurements of neutrino cross sections with the NuMI beam and several Beyond Standard Model searches. Then, ICARUS will jointly search for evidence of sterile neutrinos with the Short-Baseline Near Detector (SBND). In this contribution, we discuss recent changes to the standard TPC event reconstruction that uses Pandora, a pattern recognition software common to liquid argon-based detectors. In particular, we performed a new training of the Boosted Decision Tree (BDT) employed to separate track-like and shower-like reconstructed particles using Monte Carlo simulations of neutrino events from BNB in ICARUS. We compare the discrimination capabilities of the old and new BDT training and discuss further improvements of this algorithm.

43 PARTICLE ACCELERATORS↗

Selection of $\Lambda^0$ Production Events in MicroBooNE

A selection to find Cabibbo suppressed Λ production events, $\overline{ν}_µ$ + Ar → $µ^+$ + $Λ^0$ + $X$, in the MicroBooNE detector is described. This channel is sensitive to cross section and final state interaction parameters in different ways and can help break their degeneracy. $Λ^0$ production events are distinguished from background by the existence of a secondary vertex created when the $Λ^0$ decays. The selection examines a list of reconstructed particles thought to be the result of a neutrino interaction, first finding a muon candidate, then a proton and $π^-$ candidate and testing their consistency with a $Λ^0 → p+π^-$ decay. The selection achieves an efficiency of approximately 7% while rejecting 99.9999% of the background. This selection is designed to study neutrino interactions in the NuMI flux. After applying the selection, Monte Carlo simulation predicts 9.0 ± 0.8 (MC stat.) signal and 3.1 ± 1.4 background events when combining 1.0 × 10 21 protons on target of neutrino mode flux and 1.3 × 10 21 protons on target of anti-neutrino mode flux, corresponding to a significance of 2.6 $σ$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Laser calibration system at ProtoDUNE-HD

The Deep Underground Neutrino Experiment (DUNE) is a full experiment consisting of multiple detectors separated by a near and far site. DUNE will study long-baseline neutrino oscillations, which will provide insight into CP-violation, neutrino mass ordering, and the matter/antimatter asymmetry. Additionally, DUNE will search for nucleon decay and observe neutrinos from supernovae. DUNE relies on liquid argon time projection chambers (LAr TPCs), an excellent technology for tracking particles and reconstructing their interactions with high precision. To achieve its goals, DUNE is supported by the ProtoDUNE experiments at CERN, which serve as large-scale prototypes to validate detector technologies and calibration systems. The precise calibration of the electric field within the detector is vital for accurate 3D reconstruction of particle tracks, particularly in maintaining the consistency of charge measurements along the drift path. This work presents the development and implementation of a laser calibration system designed for ProtoDUNE-II. This poster involved the physical installation and commissioning of the laser system. The commissioning process included extensive testing to ensure alignment and operational efficiency, such as rotating the laser to observe behavior of tracks. The groundwork established during these steps is crucial for future data analysis, aiming to measure the electron lifetime (in the active volume) and map the electric field inside the detector with high precision.

Campanelli, Wallison [LIP]↗

ArCS: A Magnetized LArTPC in a Test Beam

Over the past few decades, Liquid Argon Time Projection Chambers (LArTPCs) have emerged as a central technology for rare-event detection, due to their calorimetric and imaging capabilities. Adding a magnetic field to LArTPCs would enable charge identification and momentum measurements via curvature. For neutrino experiments, this is crucial for wrong-sign neutrino rejection, electron/positron and electron/photon discrimination, and improved momentum reconstruction. The ArCS (Argon detector with Charge Separation) experiment at Fermilab's Test Beam Facility will place a 47 40 90 cm LArTPC inside a 0.7 T magnet to: (i) establish charge sign discrimination for electrons and positrons, (ii) reconstruct particle momenta via curvature, and (iii) determine the minimal magnetic field needed for these measurements. This poster will present the project status, with updates on installation and simulations of expected performance.

Cicogna, Giulia [Bologna U.]↗

Electron Neutrino Selection for Neutrino Oscillation Searches in the SBN Program

The Short- Baseline Neutrino (SBN) Pprogram at Fermilab employs a near detector, SBND, and a far detector, ICARUS, to search for baseline-dependent changes to the measure muon neutrino disappearance and electron neutrino appearance and disappearance from the muon neutrino-dominated Booster Neutrino Beam flux. The use of the same liquid argon time projection chamber (LArTPC) technology in both detectors and the same neutrino beam enables a systematically well-constrained oscillation measurement, by leveraging the high statistics of O(10 million) neutrino interactions expected at SBND through 2027 to constrain flux and cross-section systematics. In this poster, we present high-statistics, high-purity electron neutrino selections in SBND and ICARUS and related electron shower energy calibration measurements in SBND enabled by the SPINE deep learning-based particle reconstruction framework. Furthermore, we discuss how these samples can enable high sensitivity to '3+1' sterile neutrino oscillations in a combined fit accounting for systematic uncertainties and correlations.

Oza, Nupur [Nevis Labs, Columbia U.; Columbia U.]↗

Calibrating the DUNE LArTPC detectors for precision physics

The Deep Underground Neutrino Experiment (DUNE) is an international collaboration focused on studying neutrino oscillation over a long baseline (1300 km). DUNE will make use of a near detector and $\mathcal{O}$(GeV) neutrino beam originating at Fermilab in Batavia, IL, and a far detector operating 1.5 km underground at the Sanford Underground Research Facility in Lead, South Dakota. The near and far detectors will use the LArTPC (Liquid Argon Time Projection Chamber) technology to image neutrino interactions. In order to make precise physics measurements at DUNE, such as the amount of CP violation in the neutrino sector, it is essential to be able to accurately reconstruct particle energies and other kinematic quantities; this in turn necessitates an extensive calibration program for DUNE s LArTPC detectors. In this presentation, we describe the requirements for calibrating the DUNE detectors, emphasizing the challenges of massive multi-kiloton LArTPC detectors which are to operate for multiple years deep underground. A preliminary DUNE detector calibration program, including use of both dedicated calibration hardware and cosmogenic/beam-induced calibration sources, is presented. First results on detector calibration at the ProtoDUNE-SP prototype detector located at CERN, and associated impact on calibrations at the DUNE far detector, are also emphasized.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Measurement of space charge effects and energy calibration in ProtoDUNE-SP

The single-phase liquid argon prototype at CERN (ProtoDUNE-SP) acts as a validation of the design for the DUNE single-phase far detector. With a total mass of 770 tons, it is the largest monolithic liquid argon single-phase time projection chamber in the world. ProtoDUNE-SP collected test-beam in autumn of 2018 and has been collecting cosmic and special calibration data since the end of 2018.The accumulation of positive ions in a LArTPC located on the surface can distort the electric field and the reconstructed particle trajectories. It is critical to understand and correct for the space charge effects in order to achieve the desired spatial and calorimetric resolutions in the LArTPC. This talk will present the measurement of space charge effects and the calorimetric measurements of the detector in ProtoDUNE-SP.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Towards libraries of different ultrasmall amorphous silica cage topologies

Cage topology controlled at the nanometer length scale is expected to enable original functionalities, e.g., in catalysis and therapeutics. Cages are fundamental structural motifs of clathrates and their topological duals, Frank-Kasper phases, and are constituents of mesoporous silica frameworks. However, with one exception, they have not been synthesized as discrete particles. By varying reactant ratios of surfactant, oil, and silane, we report the discovery of 5(12)6(2) and 5(12)6(8) amorphous silica polyhedra, each coexisting with the previously identified 5(12) cage, using combined cryo-transmission electron microscopy (cryo-TEM) and single-particle reconstruction (SPR). Notably, the 5(12)6(8) polyhedron represents a topology not previously observed in mesoporous silica frameworks. Control over structural features is demonstrated, and insights into cage formation mechanisms are provided. Structural outcomes are summarized in a ternary morphology diagram alongside prior results, bridging serendipitous discovery and intentional design of silica cages displaying a level of control over silica polymerization rivaling nature.

Jang, Dong June [Department of Materials Science a↗

Development of a pattern recognition algorithm for reconstructing multi-particle events in the Mu2e detector

Mu2e is an upcoming experiment at Fermilab and its main goal is to search for the Charged Lepton Flavor Violation (CLFV) in the coherent transition of a muon into an electron on an Al target. In Mu2e, multi-particle events can occur simultaneously within the same time region and it is crucial to accurately identify each particle track, including signals, to improve the robustness of track finding methods and enhance reconstruction efficiency. ¯p annihilation is one of the background events and produces multiple particles that can mimic signal events. Additionally, photons from radiative pion captures can produce a γ → $e+e−$ pair, which can be used to calibrate the Mu2e momentum scale and the resolution. The Mu2e track reconstruction sequence begins by grouping hits produced in the tracker based on time and z coordinate information, called TimeCluster, and selected hits are processed to reconstruct helices and determine their momentum. The current pattern recognition algorithms identify a single helix per TimeCluster for single track events. A new pattern recognition algorithm is being developed to reconstruct multi-particle events and its features for finding multiple tracks and the current evaluation results are reported.

Kitagawa, H. [Pisa U.]↗

Parallelizing the Unpacking and Clustering of Detector Data for Reconstruction of Charged Particle Tracks on Multi-core CPUs and Many-core GPUs

We present results from parallelizing the unpacking and clustering steps of the raw data from the silicon strip modules for reconstruction of charged particle tracks. Throughput is further improved by concurrently processing multiple events using nested OpenMP parallelism on CPU or CUDA streams on GPU. The new implementation along with earlier work in developing a parallelized and vectorized implementation of the combinatoric Kalman filter algorithm has enabled efficient global reconstruction of the entire event on modern computer architectures. We demonstrate the performance of the new implementation on Intel Xeon and NVIDIA GPU architectures.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Analytic reconstruction with massive particles: one-loop amplitudes for $0\to \overline{q} qt\overline{t}H$

We present an analytic reconstruction of one-loop amplitudes for the process $0\to \overline{q} qt\overline{t}H$. Our calculation is a novel use of analytic reconstruction, retaining explicit covariance in the massive spin states through the massive spinor-helicity formalism. The analytic reconstruction relies on embedding the massive five-point kinematics in a fully massless eight-point phase space while still building a minimal ansatz directly in the five-point phase space. In order to obtain compact analytic expressions it is necessary to identify suitable partial fraction decompositions and extract common numerator factors, which we achieve through careful inspection of limits in which pairs of denominators vanish. We find that the resulting amplitudes are more numerically efficient than ones computed using automatic methods but that the gains are not as significant as in the massless case, at least at present. The method opens the door to applications at two-loop order, where numerical efficiency and improvements in the reconstruction methodology are more crucial, especially with regards to the number of free parameters in the ansatz.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

De-noising drift chambers in CLAS12 using convolutional auto encoders

Modern Nuclear Physics experimental setups run experiments with higher beam intensity resulting in increased noise in detector components used for particle track reconstruction. Increased uncorrelated signals (noise) result in decreased particle reconstruction efficiency. In this paper, we investigate the usage of Machine Learning, specifically Convolutional Neural Network Auto-Encoders (CAE), for de-noising raw hits from drift chambers in the CLAS12 detector. To the best of our knowledge, this is the first time CAE is employed to perform such an operation in this field. During the de-noising phase, it is important to remove as much noise as possible while retaining the valid hits to avoid losing crucial information about the experiment. Here, we show that using CAE, it is possible to remove noise hits while retaining up to 94% of valid tracks for a beam current of 110nA while for lower beam currents (45-55nA), we get up to 98% efficiency. Studies on experimental conditions with increasing noise show that CAE performs better than conventional tracking algorithms in isolating hits belonging to tracks. Specifically, the de-noising algorithm results in tracking efficiency improvements greater than 15%, in real data production procedures with nominal conditions, and up to two times better efficiency in synthetically generated data with high luminosity conditions (90-110nA), indicating that machine learning can lead to significantly shorter times for conducting physics experiments.

97 MATHEMATICS AND COMPUTING↗