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At least 253 records · Page 14

Combined inclusive diffractive cross sections measured with forward proton spectrometers at HERA

A combination is presented of the inclusive diffractive cross section measurements made by the H1 and ZEUS Collaborations at HERA. The analysis uses samples of diffractive deep inelastic scattering data where leading protons are detected by dedicated spectrometers. Correlations of systematic uncertainties are taken into account by the combination method, resulting in improved precision.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

On the Use of Mahalanobis Distance in Particle Image Velocimetry Post-Processing

Particle Image Velocimetry (PIV) is a method of flow measurement that has become increasingly popular as an experimental tool. New technology has made high-speed and higher-dimension (stereoscopic, tomographic, etc) methods available to an ever-growing population of researchers. These advanced methods can provide significantly more data than traditional low-speed planar PIV, but these larger data sets also require more resources to process and store. A major time sink in the post-processing of large PIV data sets is the identification and rejection of “bad” or ”spurious” vectors that survive an initial processing step in commercial software, which attempts to identify spurious vectors on an image-by-image basis and not with respect to repeated trials. The Mahalanobis distance, an almost 100-year-old statistical function, was determined to be well suited for this task for its computational efficiency and higher-dimensional nature. This work includes a summary of the Mahalanobis distance and validation of its usefulness as a tool for outlier rejection in the post-processing of PIV data.

Experimental Methods↗

Identifying Sample Provenance From SEM/EDS Automated Particle Analysis via Few-Shot Learning Coupled With Similarity Graph Clustering

Automated particle analysis (APA) provides a vast amount of compositional data via energy-dispersive X-ray spectroscopy along with size and shape data via scanning electron microscopy for individual particles in a sample. In many instances, APA data are leveraged to support identification of the source of a sample based on the detection of particles of a specific composition. Often, the particles that provide context make up a minuscule portion of the sample. Additionally, the interpretation of complex samples can be difficult due to the diversity of compositions both in the mixture and within a particle. In this work, we demonstrate a method to compute and cluster similarity graphs that describe inter-particle relationships within a sample using a multi-modal few-shot learning neural network. Here, as a proof-of-concept, we show that samples known to have been exposed to gunshot residue can be distinguished from samples occasionally mistaken for gunshot residue. Our workflow builds upon standard APA techniques and data processing methods to unveil additional information in a readily interpretable and quantitatively comparable format.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Use of nonlocal helium microplasma for gas impurities detection by the collisional electron spectroscopy method

The collisional electron spectroscopy (CES) method, which lays the ground for a new field for analytical detection of gas impurities at high pressures, has been verified. The CES method enables the identification of gas impurities in the collisional mode of electron movement, where the advantages of nonlocal formation of the electron energy distribution function (EEDF) are fulfilled. Important features of dc negative glow microplasma and probe method for plasma diagnostics are applied. A new microplasma gas analyzer design is proposed. Admixtures of 0.2% Ar, 0.6% Kr, 0.1% N{sub 2}, and 0.05% CO{sub 2} are used as examples of atomic and molecular impurities to prove the possibility for detecting and identifying their presence in high pressure He plasma (50–250 Torr). The identification of the particles under analysis is made from the measurements of the high energy part of the EEDF, where maxima appear, resulting from the characteristic electrons released in Penning reactions of He metastable atoms with impurity particles. Considerable progress in the development of a novel miniature gas analyzer for chemical sensing in gas phase environments has been made.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Hanford Tank Waste Particle Atlas

Several methods have been utilized to perform solid phase characterization. Polarized light microscopy (PLM) is used to identify individual particles based on size, shape, color, and optical properties (e.g., refractive index1, birefringence, extinction positions, and interference figures). Scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS) is used to detect which elements are present in individual particles and to infer chemical phase identification based on the metals present in combination with the size and shape of the particles. Powder X-ray diffraction (XRD) is used to identify crystalline phases present in bulk samples by matching the X-ray patterns with a library of known patterns for pure phases. Transmission electron microscopy (TEM) is used to identify individual particles by their X-ray diffraction patterns. RAMAN analysis is used to identify bulk sample compositions by matching RAMAN spectra with a library of known patterns. Other specialized techniques have not been employed routinely for Hanford tank waste samples.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Observational signatures of nonlinear magnetotail particle dynamics

It has been predicted that the nonlinear particle dynamics in the magnetotail leads to a class of resonance structures in the quiet-time ion distribution functions in the central plasma sheet (CPS). These structures exhibit a scaling law of H to the 1/4 power. The first identification of such a scaling law in quiet-time CPS distribution functions obtained aboard ISEE 1 is reported. A method is proposed which uses the observed resonance structures to determine the quiet-time current sheet thickness based on a measurement of the distribution function and magnetic field obtained by one satellite.

Chen, James↗

Direct measurement of the {sup 11}C({alpha},p){sup 14}N reaction at CRIB: A path from pp-chain to CNO

We determined the total reaction rate of the {sup 11}C({alpha},p){sup 14}N reaction relevant to the nucleosynthesis in explosive hydrogen-burning stars. The measurement was performed by means of the thick target method in inverse kinematics with {sup 11}C RI beams. We performed the identification of the ground-state transition and excited-state transitions using time-of-flight information for the first time.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Estimation of constituent properties of concrete materials with an artificial neural network based method

Multi-scale models are developed for heterogeneous concrete materials to estimate their macroscopic mechanical properties in terms of micro-structural data. One crucial challenge of those models is the identification of local properties of constituent phases. In this paper, we present an efficient method based on Artificial Neural Networks (ANN). Typical concrete materials are taken as example. A macroscopic analytical strength criterion is established from three steps of nonlinear homogenization procedure. The macroscopic strength of materials is determined as a function of the frictional coefficient and cohesion of solid cement particles at nanometer scale, intra-particle pores, inter-particle pores and aggregates (inclusions). The objective is to identify the nanoscopic frictional coefficient and cohesion of cement particle from measured macroscopic values of uniaxial compression and tensile strengths. For this purpose, a numerical method based on the ANN is developed. With the analytical macroscopic strength criterion, sensitivity studies are first realized to identify the most important micro-structural parameters influencing the macroscopic strength of concrete. A simplified analytical macroscopic strength criterion is then proposed. A large dataset is further constructed through the inversion of the analytical strength criterion by using the aggregates volume fraction, porosity, macroscopic uniaxial tensile and compressive strengths as input variables and the frictional coefficient and cohesion of cement particles as output unknowns. An ANN model containing four hidden layers and 100 neurons in each layer is constructed and trained by using this dataset. Various types of validation of the ANN model are performed. It is found that the proposed ANN based model can effectively predict the frictional coefficient and cohesion of porous cement paste at the microscopic scale with a very good accuracy.

36 MATERIALS SCIENCE↗

Interaction of nonthermal muon beam with electron-positron-photon plasma: A thermal field theory approach

Interaction of a muon beam with hot dense QED plasma is investigated. Plasma system contains electrons and positrons with Fermi-Dirac distribution and Bose-Einstein distributed photons while the beam particles have nonthermal distribution. The energy loss of the beam particles during the interaction with plasma is calculated to complete leading order of interaction in terms of the QED coupling constant using thermal field theory approach. The screening effects of the plasma are computed consistently using resummation of perturbation theory with hard thermal loop approximation according to the Braaten-Pisarski method. Time evolution of the plasma characteristics and also plasma identifications during the interaction are investigated. Effects of the nonthermal parameter of the beam distribution on the energy exchange and the evolution of plasma-beam system are also explained.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Uncertainties in estimates of the risks of late effects from space radiation

Methods used to project risks in low-Earth orbit are of questionable merit for exploration missions because of the limited radiobiology data and knowledge of galactic cosmic ray (GCR) heavy ions, which causes estimates of the risk of late effects to be highly uncertain. Risk projections involve a product of many biological and physical factors, each of which has a differential range of uncertainty due to lack of data and knowledge. Using the linear-additivity model for radiation risks, we use Monte-Carlo sampling from subjective uncertainty distributions in each factor to obtain an estimate of the overall uncertainty in risk projections. The resulting methodology is applied to several human space exploration mission scenarios including a deep space outpost and Mars missions of duration of 360, 660, and 1000 days. The major results are the quantification of the uncertainties in current risk estimates, the identification of factors that dominate risk projection uncertainties, and the development of a method to quantify candidate approaches to reduce uncertainties or mitigate risks. The large uncertainties in GCR risk projections lead to probability distributions of risk that mask any potential risk reduction using the "optimization" of shielding materials or configurations. In contrast, the design of shielding optimization approaches for solar particle events and trapped protons can be made at this time and promising technologies can be shown to have merit using our approach. The methods used also make it possible to express risk management objectives in terms of quantitative metrics, e.g., the number of days in space without exceeding a given risk level within well-defined confidence limits. Published by Elsevier Ltd on behalf of COSPAR.

Non-NASA Center↗

Uncertainties in Estimates of the Risks of Late Effects from Space Radiation

The health risks faced by astronauts from space radiation include cancer, cataracts, hereditary effects, and non-cancer morbidity and mortality risks related to the diseases of the old age. Methods used to project risks in low-Earth orbit are of questionable merit for exploration missions because of the limited radiobiology data and knowledge of galactic cosmic ray (GCR) heavy ions, which causes estimates of the risk of late effects to be highly uncertain. Risk projections involve a product of many biological and physical factors, each of which has a differential range of uncertainty due to lack of data and knowledge. Within the linear-additivity model, we use Monte-Carlo sampling from subjective uncertainty distributions in each factor to obtain a Maximum Likelihood estimate of the overall uncertainty in risk projections. The resulting methodology is applied to several human space exploration mission scenarios including ISS, lunar station, deep space outpost, and Mar's missions of duration of 360, 660, and 1000 days. The major results are the quantification of the uncertainties in current risk estimates, the identification of factors that dominate risk projection uncertainties, and the development of a method to quantify candidate approaches to reduce uncertainties or mitigate risks. The large uncertainties in GCR risk projections lead to probability distributions of risk that mask any potential risk reduction using the "optimization" of shielding materials or configurations. In contrast, the design of shielding optimization approaches for solar particle events and trapped protons can be made at this time, and promising technologies can be shown to have merit using our approach. The methods used also make it possible to express risk management objectives in terms of quantitative objective's, i.e., the number of days in space without exceeding a given risk level within well defined confidence limits.

Cucinotta, F. A.↗

Hunting Muon Neutrinos in MicroBooNE With Deep Learning Techniques

The MicroBooNE experiment is a large Liquid Argon TPC located on the Booster Neutrino Beam at Fermi National Laboratory. The primary goal is to investigate the source of the excess of low energy electron neutrino events observed by the MiniBooNE experiment. The main background to the oscillation signal arises from intrinsic electron neutrinos coming from decay of pions and muons in the beam rather than oscillation. To constrain the systematic uncertainty on this background, MicroBooNE will perform a combined fit to both the muon and electron neutrino spectra. This method provides constraints on flux, cross section, and detector uncertainties. This poster will discuss the identification and selection of muon neutrino events using a novel hybrid approach of Deep Learning and traditional reconstruction algorithms that can be used for this constraint.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Characterization of the ETEL and HZC 3-inch PMTs for the KM3NeT project

The KM3NeT collaboration constructs a multi-cubic-kilometer scale neutrino telescope in the Mediterranean Sea. The telescope’s detection units, deployed in the deep sea, will be instrumented with facet like Digital Optical Modules (DOMs), each housing 31 three-inch photomultiplier tubes (PMTs) and readout electronics inside of 17-inch pressure resistant glass sphere. Two companies, ET Enterprises Ltd (ETEL) from UK and HZC from China developed new 3-inch PMTs to meet KM3NeT requirements. 126 PMTs of types D783KFLA, D792KFLA, and D793KFLA from ETEL and 7 PMTs of type XP53 from HZC have been delivered to KM3NeT and tested by the KM3NeT groups of NIKHEF-Amsterdam and ECAP-Erlangen. Tests have been performed to measure the main PMT parameters listed in the KM3NeT specifications, such as gain, transit time spread, dark rate, fraction of spurious pulses, and quantum efficiency. Methods and results are presented in this report.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

SU-E-T-558: Monte Carlo Photon Transport Simulations On GPU with Quadric Geometry

Purpose: Monte Carlo simulation on GPU has experienced rapid advancements over the past a few years and tremendous accelerations have been achieved. Yet existing packages were developed only in voxelized geometry. In some applications, e.g. radioactive seed modeling, simulations in more complicated geometry are needed. This abstract reports our initial efforts towards developing a quadric geometry module aiming at expanding the application scope of GPU-based MC simulations. Methods: We defined the simulation geometry consisting of a number of homogeneous bodies, each specified by its material composition and limiting surfaces characterized by quadric functions. A tree data structure was utilized to define geometric relationship between different bodies. We modified our GPU-based photon MC transport package to incorporate this geometry. Specifically, geometry parameters were loaded into GPU’s shared memory for fast access. Geometry functions were rewritten to enable the identification of the body that contains the current particle location via a fast searching algorithm based on the tree data structure. Results: We tested our package in an example problem of HDR-brachytherapy dose calculation for shielded cylinder. The dose under the quadric geometry and that under the voxelized geometry agreed in 94.2% of total voxels within 20% isodose line based on a statistical t-test (95% confidence level), where the reference dose was defined to be the one at 0.5cm away from the cylinder surface. It took 243sec to transport 100million source photons under this quadric geometry on an NVidia Titan GPU card. Compared with simulation time of 99.6sec in the voxelized geometry, including quadric geometry reduced efficiency due to the complicated geometry-related computations. Conclusion: Our GPU-based MC package has been extended to support photon transport simulation in quadric geometry. Satisfactory accuracy was observed with a reduced efficiency. Developments for charged particle transport in this geometry are currently in progress.

61 RADIATION PROTECTION AND DOSIMETRY↗

Experimental study on flow characterization of a rectangular spouted bed by image processing

Systematic image processing methods are applied to characterize gas-solid flow under different operating conditions in a 30.2 mm x 101.6 mm rectangular spouted bed. Geldart D particles are imaged using a highspeed video camera. Superficial gas velocity, initial bed height, and nozzle size are investigated as influence factors. By applying adaptive thresholding and logical operation to the video frames, the original color images are transformed into binary images exposing detailed boundary information. By analyzing the binary image using a self-developed algorithm in combination with mask operations, the bed height is successfully determined, through which the variation of the bed expansion ratio with superficial gas velocity can be obtained. Additionally, the application of image morphological operations, i.e. dilation and erosion on the binarized images ensures the accurate identification of the contours of the jet and fountain so that the jet and fountain heights can be determined. Based on the image processing methods, the variation of bed expansion ratio, jet height and fountain height with the three influence factors are discussed. The current study highlights the application of image analysis in investigating the flow characterization and obtaining more hydrodynamic information in a rectangular spouted bed.

42 ENGINEERING↗

Variable rate neural compression for sparse detector data

Particle colliders produce data at extraordinary rates, posing major challenges for transmission and storage. High-throughput compression algorithms are therefore essential. In the sPHENIX experiment taking data at the Relativistic Heavy Ion Collider, a time projection chamber records three-dimensional (3D) particle trajectories that are highly sparse, making conventional learning-free lossy compression ineffective. Convolutional neural networks have surpassed traditional methods in compression ratio and accuracy. However, they fail to exploit sparsity for efficiency. To address these gaps, we present BCAE-VS, a bicephalous convolutional autoencoder with variable compression ratio for sparse data, which adapts compression to input complexity through key-point identification and sparse convolution. BCAE-VS achieves higher accuracy and compression ratios than prior neural approaches while being orders of magnitude smaller. Moreover, its throughput increases with sparsity—a property not observed in other methods. Although it was developed for collider experiments, BCAE-VS readily extends to other sparse data domains, such as light detection and ranging (LiDAR) sensing and 3D microscopy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Elucidation of Peptide-Directed Palladium Surface Structure for Biologically Tunable Nanocatalysts

Peptide-enabled synthesis of inorganic nanostructures represents an avenue to access catalytic materials with tunable and optimized properties. This is achieved via peptide complexity and programmability that is missing in traditional ligands for catalytic nanomaterials. Unfortunately, there is limited information available to correlate peptide sequence to particle structure and catalytic activity to date. As such, the application of peptide-enabled nanocatalysts remains limited to trial and error approaches. In this paper, a hybrid experimental and computational approach is introduced to systematically elucidate biomolecule-dependent structure/function relationships for peptide-capped Pd nanocatalysts. Synchrotron X-ray techniques were used to uncover substantial particle surface structural disorder, which was dependent upon the amino acid sequence of the peptide capping ligand. Nanocatalyst configurations were then determined directly from experimental data using reverse Monte Carlo methods and further refined using molecular dynamics simulation, obtaining thermodynamically stable peptide-Pd nanoparticle configurations. Sequence-dependent catalytic property differences for C-C coupling and olefin hydrogenation were then eluddated by identification of the catalytic active sites at the atomic level and quantitative prediction of relative reaction rates. This hybrid methodology provides a clear route to determine peptide-dependent structure/function relationships, enabling the generation of guidelines for catalyst design through rational tailoring of peptide sequences

atomic pair distribution function↗

Combined measurement of the inclusive diffractive cross section at HERA

A combination is presented of the inclusive diffractive cross section measurements made by the H1 and ZEUS Collaborations at HERA. The analysis uses diffractive deep inelastic scattering data selected with the Proton Spectrometer technique. Correlations of systematic uncertainties are taken into account by the combination method, resulting in improved precision.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗