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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Michel Electron Selection with SPINE for DUNE Far Detector Simulation

Michel electrons are a valuable input for particle detector calibration due to their consistent kinetic energy distribution. This report details the evaluation of a Michel electron identification method's application to simulated data from the DUNE (Deep Underground Neutrino Experiment) far detector. This method, which relies on the neural network-based particle classification software SPINE (Scalable Particle Imaging with Neural Embeddings), was developed and calibrated using simulated data for the SBND (Short-Baseline Neutrino Detector) experiment before being applied to simulated DUNE data from a 1x2x6 subset of far detector modules.

Wilson, Dante [Colorado State U.]↗
DOE OSTI2017

Improved identification of primary biological aerosol particles using single-particle mass spectrometry

Measurements of primary biological aerosol particles (PBAP), especially at altitudes relevant to cloud formation, are scarce. Single-particle mass spectrometry (SPMS) has been used to probe aerosol chemical composition from ground and aircraft for over 20 years. Here we develop a method for identifying bioaerosols (PBAP and particles containing fragments of PBAP as part of an internal mixture) using SPMS. We show that identification of bioaerosol using SPMS is complicated because phosphorus-bearing mineral dust and phosphorus-rich combustion by-products such as fly ash produce mass spectra with peaks similar to those typically used as markers for bioaerosol. We have developed a methodology to differentiate and identify bioaerosol using machine learning statistical techniques applied to mass spectra of known particle types. This improved method provides far fewer false positives compared to approaches reported in the literature. The new method was then applied to two sets of ambient data collected at Storm Peak Laboratory and a forested site in Central Valley, California to show that 0.04–2 % of particles in the 200–3000 nm aerodynamic diameter range were identified as bioaerosol. In addition, 36–56 % of particles identified as biological also contained spectral features consistent with mineral dust, suggesting internal dust–biological mixtures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Morphologically-Directed Raman Spectroscopy as an Analytical Method for Subvisible Particle Characterization in Therapeutic Protein Product Quality

Abstract Subvisible particles (SVPs) are a critical quality attribute of injectable therapeutic proteins (TPs) that needs to be controlled due to potential risks associated with drug product quality. The current compendial methods routinely used to analyze SVPs for lot release provide information on particle size and count. However, chemical identification of individual particles is also important to address root-cause analysis. Herein, we introduce Morphologically-Directed Raman Spectroscopy (MDRS) for SVP characterization of TPs. The following particles were used for method development: (1) polystyrene microspheres, a traditional standard used in industry; (2) photolithographic (SU-8); and (3) ethylene tetrafluoroethylene (ETFE) particles, candidate reference materials developed by NIST. In our study, MDRS rendered high-resolution images for the ETFE particles (> 90%) ranging from 19 to 100 μm in size, covering most of SVP range, and generated comparable morphology data to flow imaging microscopy. Our method was applied to characterize particles formed in stressed TPs and was able to chemically identify individual particles using Raman spectroscopy. MDRS was able to compare morphology and transparency properties of proteinaceous particles with reference materials. The data suggests MDRS may complement the current TPs SVP analysis system and product quality characterization workflow throughout development and commercial lifecycle.

Science & Technology - Other Topics↗
DOE OSTI2025

Concavity-based local erosion and sphere-size-based local dilation applied to lithium-ion battery electrode microstructures for particle identification

Performance metrics of lithium-ion batteries can be extracted from the analysis of electrode microstructures nanoscale imaging. The characterization workflow can involve a challenging particle identification, or instance segmentation, step. In this work, we propose a new identification method based on an original transformation: a sphere-size-based local dilation followed by a concavity-based local erosion, that is local morphology closing. The new transformation is much more efficient than the global morphology closing, with correct identification achieved with only 1.7 % dilation volume and 2.6 % erosion volume on a test geometry, compared to 39.2 % and more than 50 %, respectively, with its global counterpart. The new method has been then benchmarked versus other identification algorithms (watershed and pseudo coulomb repulsive field) on a real electrode microstructure with equal or better segmentation achieved.

25 ENERGY STORAGE↗
DOE OSTI2012

Laser methods for detecting explosive residues on surfaces of distant objects

The basic methods of laser spectroscopy that are used for standoff detection and identification of explosive traces in the form of particles on the surfaces of objects tested under real or close-toreal conditions are briefly considered. The advantages and drawbacks of all methods are discussed and their characteristics are compared. Particular attention has been given to the prospects of development and practical implementation of the technologies discussed and justification of their most preferred applications. (review)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Pileup mitigation at CMS in 13 TeV data

With increasing instantaneous luminosity at the LHC come additional reconstruction challenges. At high luminosity, many collisions occur simultaneously within one proton-proton bunch crossing. The isolation of an interesting collision from the additional “pileup” collisions is needed for effective physics performance. In the CMS Collaboration, several techniques capable of mitigating the impact of these pileup collisions have been developed. Such methods include charged-hadron subtraction, pileup jet identification, isospin-based neutral particle “δβ” correction, and, most recently, pileup per particle identification. This paper surveys the performance of these techniques for jet and missing transverse momentum reconstruction, as well as muon isolation. The analysis makes use of data corresponding to 35.9 fb−1 collected with the CMS experiment in 2016 at a center-of-mass energy of 13 TeV. The performance of each algorithm is discussed for up to 70 simultaneous collisions per bunch crossing. Significant improvements are found in the identification of pileup jets, the jet energy, mass, and angular resolution, missing transverse momentum resolution, and muon isolation when using pileup per particle identification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

First observation of antiproton annihilation at rest on argon in the LArIAT experiment

We report the first observation and measurement of antiproton annihilation at rest on argon track and shower multiplicities and particle identification conducted with the LArIAT experiment. Stopping antiprotons from the Fermilab Test Beam Facility’s charged particle test beam are identified using beamline instrumentation and LArIAT’s liquid argon time projection chamber (LArTPC). The charged particle multiplicity from the annihilation vertex is manually evaluated via hand scanning, yielding a mean of 3.2 ± 0.4 tracks and a standard deviation of 1.3 tracks, consistent with a semiautomated reconstruction resulting in 2.8 ± 0.4 tracks and a standard deviation of 1.2 tracks. Both methods are consistent with Monte Carlo simulations within statistical uncertainty. The shower multiplicities and particle identification for outgoing tracks are also consistent with eant4 model predictions. These results, obtained from a low-statistics sample, provide a foundation for higher-statistics studies in larger LArTPCs, which could refine modeling of intranuclear annihilation on argon and inform scenarios such as neutron-antineutron oscillations. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

An algorithm for charge-integration, pulse-shape discrimination and estimation of neutron/photon misclassification in organic scintillators

Organic scintillators are frequently used for measurements that require sensitivity to both photons and fast neutrons because of their pulse shape discrimination capabilities. In these measurement scenarios, particle identification is commonly handled using the charge-integration pulse shape discrimination method. This method works particularly well for high-energy depositions, but is prone to misclassification for relatively low-energy depositions. A novel algorithm has been developed for automatically performing charge-integration pulse shape discrimination in a consistent and repeatable manner. The algorithm is able to estimate the photon and neutron misclassification corresponding to the calculated discrimination parameters, and is capable of doing so using only the information measured by a single organic scintillator. This paper describes the algorithm and assesses its performance by comparing algorithm-estimated misclassification to values computed via a more traditional time-of-flight estimation. A single data set was processed using four different low-energy thresholds: 40, 60, 90, and 120 keVee. Overall, the results compared well between the two methods; in most cases, the algorithm-estimated values fell within the uncertainties of the TOF-estimated values.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2011

Prospects for X(3872) Detection at P-barANDA

Monte-Carlo simulations for a resonance scan of the charmonium-like state X(3872) at P-barANDA are performed. Final state radiation hadronic background reactions are taken into account. The signal reconstruction uses a realistic pattern recognition (track finder and track fitter) and electron/pion discrimination.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1989

A laser based computer aided non-intrusive technique for full field flow characterization in macroscopic curved channels

This paper presents the application of a laser based, computer aided image processing technique for the nonintrusive evaluation of field velocities and accelerations in a 180 deg curved channel geometry, characteristic of inlet passages of the cowls of airbreathing aircraft engines. The method based on the illumination of the test section with a laser generated planar sheet of light, allows microscopic or macroscopic surveillance of fluid flow across or along large cross sections for relatively long (greater than 1 sec) periods of time. The method is Lagrangian in concept and permits identification and tracking of the same particle, thus facilitating construction of more comprehensive trajectories and the calculation of velocities and accelerations.

Braun, M. J.↗
DOE OSTI2018

Liquid xenon scintillation measurements and pulse shape discrimination in the LUX dark matter detector

Weakly Interacting Massive Particles (WIMPs) are a leading candidate for dark matter and are expected to produce nuclear recoil (NR) events within liquid xenon time-projection chambers. Here, we present a measurement of liquid xenon scintillation characteristics in the LUX dark matter detector and develop a pulse shaped based discrimination parameter to be used for particle identification. To accurately measure the scintillation characteristics, we develop a template-fitting method to reconstruct the detection time of photons. Analyzing calibration data collected during the 2013-16 LUX WIMP search, we measure a singlet-to-triplet scintillation ratio for electron recoils (ER) that is consistent with existing literature, and we make a first-ever measurement of the NR singlet-to-triplet ratio at recoil energies below 74 keV. A prompt fraction discrimination parameter exploits the difference of the photon time spectra for NR and ER events and is optimized to have the least number of ER events that occur in the 50% NR acceptance region. When this discriminator is used in conjunction with charge-to-light discrimination on the calibration data, the signal-to-noise ratio in the NR dark matter acceptance region increases by up to a factor of two.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Likelihood-Based Particle Identification in the Short-Baseline Near Detector

Accurate particle identification is crucial in any high-energy physics experiment, allowing scientists to understand the unique interactions and mechanisms at play in a detector. In this project, I develop and study a new particle identification (PID) algorithm for the Short-Baseline Near Detector, a likelihood-based approach, different from out current $\chi^2$ method. A likelihood estimation offers a more physically motivated strategy for PID. The distribution random energy losses of charged particles traveling through a medium are described by the Vavilov probability density function. By using this model, we can account for random energy losses and construct likelihood functions specific to each particle type, potentially enabling a more accurate method for PID.

Vanderwaal, Sophia [U. Alabama, Huntsville] (ORCID↗
DOE OSTI2023

Development of ML FPGA Filter for Particle Identification and Tracking in Real Time

Real-time data processing is a frontier field in experimental particle physics. Machine Learning methods are widely used and have proven to be very powerful in particle physics. The growing computational power of modern FPGA boards allows us to add more sophisticated algorithms for real time data processing. Many tasks could be solved using modern Machine Learning (ML) algorithms which are naturally suited for FPGA architectures. The FPGA-based machine learning algorithm provides an extremely low, sub-microsecond, latency decision and makes information-rich data sets for event selection. We report work has started to evaluate an FPGA based Machine Learning (ML) algorithm for a real-time particle identification and tracking with Transition Radiation Detector (TRD) and e/m calorimeter. The first target is the GlueX experiment, with a plan to build a TRD based on GEM technology. GlueX trigger latency is 3.3 μs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Defect and satellite characteristics of additive manufacturing metal powders

Metal additive manufacturing (AM) requires high-quality metal powders to three-dimensionally (3D) print metallic components with complex and customizable geometries. The lack of quantification of AM metal powders creates quality control challenges for 3D printed components, increases the uncertainty of printing reliability and net cost of inspected and certified printed components, and reduces the recyclability of used powders. However, critical characteristics of AM metal powders that are decisive factors for the 3D printing process, such as internal porosity, contamination, and satellite feature, remain ambiguous. In this work, we developed a novel approach to 3D quantify key characteristics of AM metal powders down to individual particles by using high-resolution synchrotron x-ray computed tomography. Empowered by the penetrative capability of high-energy x-ray, internal porosity and contamination within as-atomized metal powders from high-entropy alloys to nickel-based superalloys were evaluated. Additionally, the newly-developed dispersion method enables the homogeneous separation of individual particles, and consequently, results in the implementation of 3D particle shape analysis. To resolve a major challenge of identification and quantification of satellite-feature particles in as-atomized AM metal powders, the satellite features were quantitated by modeling and analyzing the shape parameter of local thickness variance. Furthermore, the 3D analytical methods of particle assessment in this study can be applied to other materials systems like rock, food, and pharmaceutical particles, and provide insights for process optimization across powder metallurgy, concrete, food and pharmaceutical manufacturing, and AM industries.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Diffractive Dissociation into {pi}{sup -{pi}-{pi}+} Final States at COMPASS

QCD predicts gluonic excitations like hybrids to contribute to the meson spectrum in addition to qq-bar pair configurations. The most promising way to identify such states is the search for J{sup PC} quantum number combinations which are forbidden in the constituent quark model. The fixed target COMPASS experiment at CERN offers the opportunity to search for such states in the light quark sector with an unprecedented statistics.Diffractive reactions of 190 GeV/c pions on a lead target were studied by COMPASS during a pilot run in 2004. A Partial Wave Analysis (PWA) of the {pi}{sup -{pi}-{pi}+} final state with 42 waves including acceptance corrections through a phase-space Monte Carlo simulation of the spectrometer was performed. The exotic {pi}{sup 1}(1600) meson with quantum numbers J{sup PC} = 1{sup -+} has been clearly established in the rho-pi decay channel with a mass of 1660{+-}10(stat) MeV/c{sup 2} and a width of 269{+-}21(stat) MeV/c{sup 2}. The improved detector performance in 2008 allows us to study this channel with significantly higher statistics. First results of the ongoing analysis of the 2008 data taking period, using a 190 GeV/c pion beam on a liquid hydrogen target are presented in this paper.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2013

Background studies for NaI(Tl) detectors in the ANAIS dark matter project

Several large NaI(Tl) detectors, produced by different companies, have been operated in the Canfranc Underground Laboratory (LSC) in the frame of the ANAIS (Annual modulation with NaI Scintillators) project devoted to the direct detection of dark matter. A complete background model has been developed for a 9.6 kg detector (referred as ANAIS-0 prototype) after a long data taking at LSC. Activities from the natural chains of {sup 238}U and {sup 232}Th, and {sup 40}K in the NaI(Tl) crystal were evaluated applying different methods: discrimination of alpha particles vs beta/gamma background by Pulse Shape Analysis for quantifying the content of the natural chains and coincidence techniques for {sup 40}K. Radioactive contaminations in the detector and shielding components were also determined by HPGe spectrometry. Monte Carlo simulations using Geant4 package were carried out to evaluate their contribution. At high energies, most of the measured background is nicely reproduced; at low energy some non-explained components are still present, although some plausible background sources have been analyzed. The {sup 40}K content of the NaI(Tl) crystal has been confirmed to be the dominant contributor to the measured background with this detector. In addition, preliminary results of the background characterization, presently underway at the LSC, of two recently produced NaI(Tl) detectors, with 12.5 kg mass each, will be presented: cosmogenic induced activity has been clearly observed and is being quantified, and {sup 40}K activity at a level ten times lower than in ANAIS-0 has been determined.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Resonance interaction in LBNE fine-grained-tracker near detector

This talk is devoted to resonance interaction (RES) in the proposed fine-grained tracker detector (FGT) for LBNE experiment. We use fast MC to study the sensitivity of FGT to RES, and use this measurement as a handle to constrain nuclear effects. Similar analysis is performed on NOMAD data for validation and better understanding. Preliminary RES measurement result using NOMAD data will be reported.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model

The cells in battery energy storage systems are monitored, protected, and controlled by battery management systems whose sensors are susceptible to cyberattacks. False data injection attacks (FDIAs) targeting batteries’ voltage sensors affect cell protection functions and the estimation of critical battery states like the state of charge (SoC). Inaccurate SoC estimation could result in battery overcharging and over discharging, which can have disastrous consequences on grid operations. This paper proposes a three-pronged online and offline method to detect, identify, and classify FDIAs corrupting the voltage sensors of a battery stack. To accurately model the dynamics of the series-connected cells a single particle model is used and to estimate the SoC, the unscented Kalman filter is employed. FDIA detection, identification, and classification was accomplished using a tuned cumulative sum (CUSUM) algorithm, which was compared with a baseline method, the chi-squared error detector. Online simulations and offline batch simulations were performed to determine the effectiveness of the proposed approach. Throughout the batch simulations, the CUSUM algorithm detected attacks, with no false positives, in 99.83% of cases, identified the corrupted sensor in 97% of cases, and determined if the attack was positively or negatively biased in 97% of cases.

25 ENERGY STORAGE↗