Determination of the Lightest Strange Resonance K 0 * ( 700 ) or κ , from a Dispersive Data Analysis
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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
SEARCH · Engineering Papers
Results for “Particle data analysis”
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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Lead Isotope Fingerprinting of Nanoscale Mineral Particles via Single Particle Inductively Coupled Plasma Mass Spectrometry
Determining lead (Pb) isotopic ratios is of broad interest across chemical disciplines for source tracing and age determinations, but the technique is inherently limited when multiple sources of Pb are present in a single sample. Single particle methods offer a solution to directly resolve multiple distinct isotopic ratios within individual samples. In this study, single particle inductively coupled plasma-mass spectrometry (spICP-MS) was performed using time-of-flight (TOF) and multicollector (MC)-based platforms to measure the Pb isotopic composition of individual nanoscale particles. Distinct Pb isotope ratios were measured in particles from four powdered galena samples of different origins (average single-particle 206Pb/207Pb ratios ranging from 0.91 to 1.28) using both the TOF and the MC-based platforms. Differentiation of galena particles in a mixture (liberated grains and those hosted in a silicate matrix) was possible due to detecting their unique multielemental fingerprints. The differing behavior of the two galena samples during digestion was investigated; undigested galena particles trapped within silicate minerals were detected using spICP-MS, which has implications for Pb recovery in bulk-scale isotopic analysis. The multinuclide detection capability of the ICP-TOF-MS instrument allowed for the simultaneous detection of secondary constituent elements within the nanoparticles and the identification of multiple populations of isotopically distinct Pb-bearing particles in a copper ore sample, whereas the increased sensitivity of the MC instrument enabled quantification of 204Pb, allowing differentiation using multiple isotopic ratios. A single particle isotope ratio analysis module was developed within an open-source spICP-TOF-MS data processing platform, enabling its adoption across chemical disciplines.
Goodman, Aaron J. [University of Montreal, Quebec,↗
An ultra-fast method for generating synthetic down-scattered neutron data for inertial confinement fusion implosions
In inertial confinement fusion experiments at the National Ignition Facility, asymmetries are probed by a variety of neutron diagnostics, including neutron imaging systems, real-time neutron activation diagnostics (RTNADs), and neutron spectrometers. It is often useful to generate synthetic data based on these diagnostics to validate and tune models. However, current methods of doing so using Monte Carlo particle tracing are time-consuming. In this paper, an ultra-fast method is presented for generating synthetic neutron images, RTNAD data, and spectrometry data using line integrals and 3D convolutions. While it does not contain as much physics as particle tracing codes, it is thousands of times faster and produces nearly identical data. This enables analysis techniques that depend on generating large amounts of synthetic data, which will prove very useful for the study of asymmetries going forward.
Deuterium↗
Modelling dust transport in DIII-D with DTOKS-Upgrade
Comprehensive upgrades to the dust transport code Dust in TOKamaks (DTOKS) that extend the plasma-dust interaction model are presented and compared with recent measurements of dust transport in DIII-D. Simulations incorporating variation in physical properties of graphite dust with temperature and size in a stationary plasma background suggest a substantial decrease in lifetimes due principally to thermal expansion. The trajectories of 53 dust grains identified from analysis of visible camera data taken across two similar shots were used to measure the dust particle velocity distributions. Dust tracks terminated mostly at the outer divertor strike point having a mean observation time of 2.1 ± 0.4 ms. Stochastic modelling of 200 graphite dust particles in the DIII-D tokamak performed with DTOKS-U using plasma simulations generated by OEDGE found similar behaviour, with particles ablating rapidly after acquiring a positive charge in the region close to the outer strike point, creating an acute source of neutral carbon atoms. The simulated mean lifetime, 11 ± 2 ms, showed approximate agreement with experimental observation when corrected by accounting for dust visibility and ignoring the longest trajectories 1.5 ± 0.2 ms. Synthetic diagnostic data generated from coupling the results of DTOKS-U with the visualisation software Calcam offers a powerful new tool for validation of simulations and predictive calculations of dust dynamics.
Physics↗
Time at Temperature Abstract and Intern Poster
Current NRC regulations for BWR operations dictate that any occurrence reaching the point of Departure from Nucleate Boiling (DNB) disqualifies the use of the fuel rod for further reactor operation. That criteria does not account for duration of rate of the power increase, the corresponding effects on material properties or rewetting that may occur. Previous Anticipated Operational Occurrences (AOOs) show short durations power increase that may require limited amounts of heat removal. Industrial experience has shown evidence that fuel can reach dryout yet continue to safely operate in regular reactor conditions. The gap in research into such occurrences is the motivation for a series of experiments, including this current work. Time-at-Temperature experiments aim to identify and characterize the microstructural changes in Zircaloy-2 under oxygen-free high temperatures. This work uses the FlashDSC instrument to rapidly ramp up and down the temperature of a focus ion beam (FIB) prepared large area lift-out (LALO) of Zircaloy-2 at a rate of 10,000 K/s to desired values. The focus is to characterize the microstructure evolution, if any, of Zircaloy-2 that may impact its performance under typical BWR conditions. This characterization includes analyzing grain structure and size, secondary phase particle (SPPs) size, shape, composition, and location using Transmission Electron Microscopy (TEM). Further data collection and analysis is in progress including diffraction pattern indexing and 4D STEM processing. While current results focus on the testing and characterizing unirradiated material, future plans include expansion to irradiated material to explore the effects of rapid transition rates seen in DNB and dryout conditions on irradiationg damage and defect annealing.
36 - MATERIALS SCIENCE↗
Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data
The dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D fast Fourier transforms, correlation, and pair distribution functions are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. Here, the deep learning (DL)-based workflow is developed to analyze detailed particle dynamics and explore the evolution of local geometries. Finally, we use a combination of DL feature extraction and mixture modeling to define particle neighborhoods free of physics constraints, allowing for a separation of possible classes of particle behavior and identification of the associated transitions. Overall, this work establishes the workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.
36 MATERIALS SCIENCE↗
Studying the hadron structure with PANDA and CLAS using machine learning techniques
The hadron spectroscopy and structure are currently very active fields of research to study the non-perturbative regime of quantum chronodynamics. The first one studies the complex structure of excited hadrons by looking at their decay products, while the latter uses lepton scattering on nucleons. Both methods require reconstruction algorithms with great efficiency and good particle identification and background rejection rates. This work aims to provide these by either improving the existing methods or developing new ones. The first part of this document presents a feasibility study of a predicted hybrid charmonium state for the PANDA experiment. Lattice QCD calculations predict the ground state hybrid charmonium to be a spin exotic with quantum numbers of JP C = 1?+ at a mass of around 4.3 GeV with a width to be around 20 MeV. A machine learning based data analysis scheme is proposed to further improve the signal efficiency and the background reduction, alongside with improvements of the analysis software (PandaRoot), that are vital for this study. These improvements include a reworked clustering algorithm for the electromagnetic calorimeter (EMC) and an optimized monte carlo matching for neutral particles. The second part of this document is about studying the proton structure. A multidimensional study of the structure function ratio Fsin(?)LU /FUU has been performed for K±, based on the measurement of beam-spin asymmetries. It uses the high statistics data recorded with the CLAS12 spectrometer at Jefferson Laboratory. Fsin(?)LU is a twist-3 quantity that provides information about the quark gluon correlations in the proton. This document will present for the first time a simultaneous analysis of two kaon channels over a large kinematic range of z, xB , PT and Q2 with virtualities Q2 ranging from 1 GeV2 up to 8 GeV2 using machine learning techniques for improved particle identification.
Kripko, Aron↗
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 ↗
Micro-tensile characteristics of As-fabricated and irradiated AGR-2 TRISO fuel particle buffer, IPyC, and buffer-IPyC interlayer regions
A recently developed micro-tensile sample preparation technique was implemented to evaluate the tensile strengths of the buffer, IPyC, and buffer-IPyC interlayer regions of the unirradiated and irradiated AGR-2 TRISO fuel particles. Understanding the mechanical properties of the buffer-IPyC interlayer is essential for developing thermomechanical models of buffer-IPyC separation, yet there is a lack of experimental data on its micro-tensile properties. TEM analysis was conducted on these regions to determine the microstructural changes relevant to the samples' tensile properties. In the unirradiated TRISO particle samples, the buffer layer demonstrated the weakest tensile strength, while the IPyC layer exhibited the highest. Conversely, in the irradiated TRISO particle samples, the buffer-IPyC interlayer region showed the lowest tensile strength, with the IPyC layer being the strongest. Fractures in the samples from the buffer-IPyC region predominantly occurred either in the buffer layer or at the buffer-IPyC interface. However, some buffer-IPyC interlayer samples displayed stress-strain and fracture behaviors more akin to the IPyC layer than the buffer layer. Analysis of diffraction patterns suggests that irradiation may have increased anisotropy in the three regions tested. Despite this suggested increase in anisotropy, there was no evidence that it affected the measured strengths. The irradiated TRISO particles demonstrated a considerable increase in void space and a decrease in ultimate tensile strength within the buffer-IPyC interlayer region due to the densification and contraction of the buffer layer. Minor variations in diffraction ring patterns were also observed. These changes, coupled with a significant reduction in the Weibull modulus/shape parameter, imply that irradiation-induced densification leads to tearing between the buffer and IPyC layers at locations of elevated porosity in the buffer-IPyC interlayer region.
Tristructural isotropic (TRISO)↗
Evolution of standardization and dissemination of cryo-EM structures and data jointly by the community, PDB, and EMDB
Cryogenic electron microscopy (cryo-EM) methods began to be used in the mid-1970s to study thin and periodic arrays of proteins. Following a half-century of development in cryo-specimen preparation, instrumentation, data collection, data processing and modeling software, cryo-EM has become a routine method for solving structures from large biological assemblies to small biomolecules at near to true atomic resolution. This review explores the critical roles played by the Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB) in partnership with the community to develop the necessary infrastructure to archive cryo-EM maps and associated models. Public access to cryo-EM structure data has in turn facilitated better understanding of structure-function relationships and advancement of image processing and modeling tool development. The partnership between the global cryo-EM community and PDB and EMDB leadership has synergistically shaped the standards for metadata, one-stop deposition of maps and models, and validation metrics to assess the quality of cryo-EM structures. The advent of cryo-electron tomography (cryo-ET) for in situ molecular cell structures at a broad resolution range and their correlations with other imaging data introduces new data archival challenges in terms of data size and complexity in the years to come.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Fuel Stratification Influence on NO x Emission in a Premixed Axial Reacting Jet-in-Crossflow at High Pressure
Three reacting jet-in-crossflow (JiC) methane/air flames were numerically investigated in a lean axially staged combustor at a pressure of five atmospheres. A detailed chemistry Star-CCM+ computational fluid dynamics (CFD) model was used with 53 species considered and the result of turbulence-governed finite-rate modeling was validated with in-house experimental data. An optically accessible test section features three side windows, allowing local flow and flame analysis with particle image velocimetry (PIV) and CH* chemiluminescence as well as pressure, temperature, and species exit measurements. The research objective was to predict and verify NO x formation of the premixed 12.7 mm axial jet. Three headend temperature levels were investigated along with three premixed jets at lean (φ Jet = 0.75), near-stoichiometric (φ Jet = 1.07), and rich (φ Jet = 1.78) axial fuel line equivalence ratio. Based on the matching exit emission concentration, global emission benefits were investigated by adjustment of the fuel stratification. The perfectly premixed methane/air flames of this study were shown to ignite at the lee-side of the jet. For the elevated headend temperature level T = 1800 K, the flame extended beyond the windward jet trajectory and caused high axial NO production. For industry application, a firing temperature of 1920 K was achieved with a NO x optimized fuel split of 25%, combining a lean headend (φ Headend = 0.61) with a rich (φ Jet = 1.78) jet equivalence ratio. As a result, this operating point allowed minimization of the combustor residence time at temperatures above 1700 K as well as combustion in a compact flame at the jet lee-side along the counter rotating vortex pair.
03 NATURAL GAS↗
Three-dimensional super line-localization in low signal-to-noise microscope images via prior-apprised unsupervised learning (PAUL)
Biological processes such as processive enzyme turnover and intracellular cargo tracking involve the dynamic motion of a small "article" along a curvilinear biopolymer track. To understand these processes that occur across multiple length and time scales, one must acquire both the trajectory of the particle and the position of the track along which it moves, possibly by combining high-resolution single-particle tracking with conventional microscopy. Yet, usually there is a significant resolution mismatch between these modalities: while the tracked particle is localized with a precision of 10 nm, the image of the surroundings is limited by optical difraction, with 200 nm lateral and 500 nm axial resolutions. Compared to the particle's trajectory, the surrounding curvilinear structure appears as a blurred and noisy image. This disparity in the spatial resolutions of the particle trajectory and the surrounding curvilinear structure image makes data reconstruction, as well as interpretation, particularly challenging. Analysis is further complicated when the curvilinear structures are oriented arbitrarily in 3D space. Here, we present a prior-apprised unsupervised learning (PAUL) approach to extract information from 3D images where the underlying features resemble a curved line such as a filament or microtubule. This three-stage framework starts with a Hessian-based feature enhancement, which is followed by feature registration, where local line segments are detected on repetitively sampled subimage tiles. In the final stage, statistical learning, segments are clustered based on their geometric relationships. Principal curves are then approximated from each segment group via statistical tools including principal component analysis, bootstrap and kernel transformation. This procedure is characterized on simulated images, where sub-voxel medium deviations from true curves have been achieved. The 3D PAUL approach has also been implemented for successful line localization in experimental 3D images of gold nanowires obtained using a multifocal microscope. Lastly, this work not only bridges the resolution gap between two microscopy modalities, but also allows us to conduct 3D super line-localization imaging experiments, without using super-resolution techniques.
42 ENGINEERING↗
Search for Reactor-Produced Millicharged Particles with Skipper-CCDs at the CONNIE and Atucha-II Experiments
Millicharged particles, proposed by various extensions of the standard model, can be created in pairs by high-energy photons within nuclear reactors and can interact electromagnetically with electrons in matter. Recently, the existence of a plasmon peak in the interaction cross section with silicon in the eV range was highlighted as a promising approach to enhance low-energy sensitivities. The CONNIE and Atucha-II reactor neutrino experiments utilize Skipper-CCD sensors, which enable the detection of interactions in the eV range. We present world-leading limits on the charge of millicharged particles within a mass range spanning 6 orders of magnitude, derived through a comprehensive analysis and the combination of data from both experiments. Published by the American Physical Society 2025
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Publishing unbinned differential cross section results
Machine learning tools have empowered a qualitatively new way to perform differential cross section measurements whereby the data are unbinned, possibly in many dimensions. Unbinned measurements can enable, improve, or at least simplify comparisons between experiments and with theoretical predictions. Furthermore, many-dimensional measurements can be used to define observables after the measurement instead of before. There is currently no community standard for publishing unbinned data. While there are also essentially no measurements of this type public, unbinned measurements are expected in the near future given recent methodological advances. The purpose of this paper is to propose a scheme for presenting and using unbinned results, which can hopefully form the basis for a community standard to allow for integration into analysis workflows. This is foreseen to be the start of an evolving community dialogue, in order to accommodate future developments in this field that is rapidly evolving.
47 OTHER INSTRUMENTATION↗
Bose-Einstein correlations of charged hadrons in proton-proton collisions at $\sqrt{s} =$ 13 TeV
Bose-Einstein correlations of charged hadrons are measured over a broad multiplicity range, from a few particles up to about 250 reconstructed charged hadrons in proton-proton collisions at $ \sqrt{s} $ = 13 TeV. The results are based on data collected using the CMS detector at the LHC during runs with a special low-pileup configuration. Three analysis techniques with different degrees of dependence on simulations are used to remove the non-Bose-Einstein background from the correlation functions. All three methods give consistent results. The measured lengths of homogeneity are studied as functions of particle multiplicity as well as average pair transverse momentum and mass. The results are compared with data from both CMS and ATLAS at $ \sqrt{s} $ = 7 TeV, as well as with theoretical predictions.[graphic not available: see fulltext]
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
An Efficient, Scalable IO Framework for Sparse Data: larcv3
Neutrino physics is one of the fundamental areas of research into the origins and properties of the Universe. Many experimental neutrino projects use sophisticated detectors to observe properties of these particles, and have turned to deep learning and artificial intelligence techniques to analyze their data. From this, we have developed \texttt{larcv}, a \texttt{C++} and \texttt{Python} based framework for efficient IO of sparse data with particle physics applications in mind. We describe in this paper the \texttt{larcv} framework and some benchmark IO performance tests. \texttt{larcv} is designed to enable fast and efficient IO of ragged and irregular data, at scale on modern HPC systems, and is compatible with the most popular open source data analysis tools in the Python ecosystem.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
How data science methods can improve the quality and efficiency of ICF and HEDP research
Data Science methods (many that are Bayesian based) are widely used in the physical sciences to estimate model parameters from experimental data, synthesize heterogeneous data, calibrate models, design experiments, and determine statistical significance of data. These methods provide a wealth of advantages over traditional analysis techniques because: 1) uncertainties are rigorously defined and propagated naturally through complex systems including covariance, 2) prior information is captured within the analysis framework (including rad-MHD and rad-hydro simulations), 3) competing models can be selected and/or ruled out using quantitative criteria, and 4) complex, heterogeneous data can be incorporated simultaneously. While these methods have been widely adopted as the gold standard in fields such as particle physics, astronomy, and biology, they have been slow to catch on in Inertial Confinement Fusion (ICF) and High Energy Density Physics (HEDP) research. Recently, several teams at LLNL, SNL, LANL, and the LLE have been exploring the use of these tools in their research and have found success. Here we propose that a concerted effort to consolidate these independent research efforts by developing and deploying common tools for use across the complex can revolutionize the way we approach data analysis, assimilation of theory and experiment, and decision making. The Bayesian formalism provides a means to accomplish this, but we are lacking certain infrastructure to make it happen on a large scale. Furthermore, once adopted, these techniques can be used to develop standards by which discoveries can be judged, similar to the so-called 5σ rule in high energy particle physics. Such standards may be used in the future to address the issue of unknown reproducibility in ICF and HED experiments caused by low shot rate and high cost per experiment. Our goals as a group are to advance the state of the art in HED measurement science by enabling: 1) better inferences from data with well-defined uncertainties, 2) better use of the data we have and continue to collect, 3) intelligent synthesis of data, 4) evaluation of the statistical significance of our data, and 5) informed decision making regarding the design of new experiments and instruments.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Non-asymptotic analysis of ensemble Kalman updates: effective dimension and localization
Many modern algorithms for inverse problems and data assimilation rely on ensemble Kalman updates to blend prior predictions with observed data. Ensemble Kalman methods often perform well with a small ensemble size, which is essential in applications where generating each particle is costly. This paper develops a non-asymptotic analysis of ensemble Kalman updates, which rigorously explains why a small ensemble size suffices if the prior covariance has moderate effective dimension due to fast spectrum decay or approximate sparsity. Here, we present our theory in a unified framework, comparing everal implementations of ensemble Kalman updates that use perturbed observations, square root filtering and localization. As part of our analysis, we develop new dimension-free covariance estimation bounds for approximately sparse matrices that may be of independent interest.
Mathematics↗