Search for New Phenomena in Final States with Two Leptons and One or No b -Tagged Jets at s = 13 TeV Using the ATLAS Detector
Not Available
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
SEARCH · Engineering Papers
Results for “particle data”
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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Measurements of Pion and Muon Nuclear Capture at Rest on Argon in the LArIAT Experiment
We report the measurement of the final-state products of negative pion and muon nuclear capture at rest on argon by the LArIAT experiment at the Fermilab Test Beam Facility. We measure a population of isolated MeV-scale energy depositions, or blips, in 296 LArIAT events containing tracks from stopping low-momentum pions and muons. The average numbers of visible blips are measured to be 0.74 ± 0.19 and 1.86 ± 0.17 near muon and pion track endpoints, respectively. The 3.6𝜎 statistically significant difference in blip content between muons and pions provides the first demonstration of a new method of pion-muon discrimination in neutrino liquid argon time projection chamber experiments. LArIAT Monte Carlo simulations predict substantially higher average blip counts for negative muon (1.22 ± 0.08) and pion (2.34 ± 0.09) nuclear captures. We attribute this difference to geant4’s inaccurate simulation of the nuclear capture process.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Neural refinement of sample weights
Monte Carlo simulations are an essential tool in particle physics data analysis. Events are typically generated alongside weights that redistribute the cross section of the simulated process across the phase space. These weights can be negative, and several post hoc methods have been developed to eliminate or mitigate the negative values. All of these methods share the common strategy of approximating the average weight as a function of phase space. We introduce an alternative approach, which, instead of reweighting to the average, refines the initial weights with a scaling transformation, utilizing a phase space-dependent factor. Since this new refinement method does not need to model the full weight distribution, it can be more accurate. High-dimensional and unbinned phase space is processed using neural networks for the refinement method. In addition to the refinement method, we introduce a new resampling protocol, which can be used in conjunction with any weight transformation to not only preserve the average weight but also the statistical uncertainties of the initial distribution. Using both realistic and synthetic examples, we show that the new neural refinement method is able to match or exceed the accuracy of similar weight transformations and that the new resampling protocol is simpler in implementation than previous methods while exhibiting equivalent statistical properties.
Artificial neural networks↗
Particle-Tracking Proton Computed Tomography—Data Acquisition, Preprocessing, and Preconditioning
Proton CT (pCT) is a promising new imaging technique that can reconstruct relative stopping power (RSP) more accurately than x-ray CT in each cubic millimeter voxel of the patient. This, in turn, will result in better proton range accuracy and, therefore, smaller planned tumor volumes (PTV). The hardware description and some reconstructed images have previously been reported. In a series of two contributions, we focus on presenting the software algorithms that convert pCT detector data to the final reconstructed pCT images for application in proton treatment planning. There were several options on how to accomplish this, and we will describe our solutions at each stage of the data processing chain. In the first paper of this series, we present the data acquisition with the pCT tracking and energy-range detectors and how the data are preprocessed, including the conversion to the well-formatted track information from tracking data and water-equivalent path length from the data of a calibrated multi-stage energy-range detector. These preprocessed data are then used for the initial image formation with an FDK cone-beam CT algorithm. The output of data acquisition, preprocessing, and FDK reconstruction is presented along with illustrative imaging results for two phantoms, including a pediatric head phantom. The second paper in this series will demonstrate the use of iterative solvers in conjunction with the superiorization methodology to further improve the images resulting from the upfront FDK image reconstruction and the implementation of these algorithms on a hybrid CPU/GPU computer cluster.
42 ENGINEERING↗
Dynamics of QCD matter — current status
In this study, there are 18 sections discussing various current topics in the field of relativistic heavy-ion collisions and related phenomena, which will serve as a snapshot of the current state of the art. Section 1 reviews experimental results of some recent light-flavored particle production data from ALICE collaboration. Other sections are mostly theoretical in nature. Very strong but transient magnetic field created in relativistic heavy-ion collisions could have important observational consequences.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Gwaihir: Jupyter Notebook graphical user interface for Bragg Coherent Diffraction Imaging
Data on particle II-A1, 450°C, under CO and Argon atmosphere, 002 reflection, 1.818 detector distance, Eiger detector.
Bragg Coherent Diffraction Imaging↗
picassodev/picasso
Picasso is a C++ template library for the development of exascale-class particle-in-cell methods. It contains data structures for particle and grid management using the ECP Kokkos and Cabana libraries, abstractions for particle and grid discretizations, and tools for interface tracking and reconstruction with particles.
Slattery, Stuart↗
High-dimensional and permutation invariant anomaly detection
Methods for anomaly detection of new physics processes are often limited to low-dimensional spaces due to the difficulty of learning high-dimensional probability densities. Particularly at the constituent level, incorporating desirable properties such as permutation invariance and variable-length inputs becomes difficult within popular density estimation methods. In this work, we introduce a permutation-invariant density estimator for particle physics data based on diffusion models, specifically designed to handle variable-length inputs. We demonstrate the efficacy of our methodology by utilizing the learned density as a permutation-invariant anomaly detection score, effectively identifying jets with low likelihood under the background-only hypothesis. To validate our density estimation method, we investigate the ratio of learned densities and compare to those obtained by a supervised classification algorithm.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Fission TPC cross section ratio results [Abstract]
The fission TPC was designed for precision fission cross section ratio measurements. The high-fidelity 3D particle tracking data is used to conduct a detailed analysis of the uncertainty contributions. In this contribution a brief overview of the instrument was presented. The results of the 238 U(n,f)/ 235 U(n,f) cross section ratio measurement published in [Phys. Rev C 97, 034618 (2018)] were reviewed and the preliminary status of the 239 Pu(n,f)/ 235 U(n,f) cross section ratio measurement was shown.
238U(n,f)/235U(n,f) cross section ratio↗
Nuclear Data Evaluation Improvements for Charged Particle Transport [Slides]
Abstract not provided.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Isomers and Dark Matter: 180m Ta and 178m Hf [Slides]
Dark matter has attractive gravitational interactions. It does not interact with light, or has very small electromagnetic coupling. It is stable, or has a lifetime much longer than the age of the Universe. No departure model from the law of gravity explains all data, whereas particle dark matter can.
79 ASTRONOMY AND ASTROPHYSICS↗
Density functional theory (DFT) study of UF 6 hydrolysis: reaction pathways, spectroscopy, and chemical kinetics
Depleted uranium hexafluoride (UF 6 ), a stockpiled byproduct of the nuclear fuel cycle, reacts readily with atmospheric humidity, but the gas-phase reaction mechanism and associated chemical kinetics are poorly understood. During the performance period we undertook development of a state-of-the-art ab initio gas-phase chemical kinetics simulation workflow to model the hydrolysis of uranium hexafluroride (UF 6 ). In doing so, we addressed several outstanding issues in the theoretical treatment of uranium-containing systems. At the outset it was unclear how to generate accurate estimates of kinetic and thermodynamic data for U-containing chemical reactions. Generation of such data has been made routine. Prior to our work, the literature associated with UF 6 hydrolysis were disparate and inaccurate. This body of work provides a modern and comprehensive theoretical assessment of the reaction mechanism, molecular clustering towards deposition, and chemical kinetics. New methodological implementations and software integrations resulting from this work are also highlighted. As much as possible, our predictions were validated against experimental data including particle morphologies, vibrational spectroscopy, atomization enthalpies, and kinetic rate constants. Nevertheless, we were unable to reconcile kinetic measurements with high-accuracy simulations.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Predicting cutoff L-shells of solar protons using the GPPSn particle dataset
Solar energetic protons (SEPs) arriving at the Earth trigger severe radiation storms in the near-Earth space, directly impacting space missions operating at various altitudes. Therefore, monitoring SEP events and predicting the penetration depths of solar protons are critical for aerospace sectors. Building on previous efforts, here we demonstrate the feasibility of using proton measurements from the Global Prompt Proton Sensor network (GPPSn), enabled by Los Alamos National Laboratory developed combined X-ray dosimeters aboard GPS satellites, to characterize and predict the penetration of solar protons into the geomagnetic field. The inclined medium-Earth-orbits (MEOs) of the global GPS constellation offer a unique advantage of allowing simultaneous measurements of penetrating solar protons inside both open- and closed-field line regions. Therefore, the L-profiles of ∼10s–100 MeV solar protons and their associated cutoff L-shells can be determined from the GPPSn dataset, using predefined threshold proton flux values rather than traditional flux ratios. After examining a list of SEP event intervals across solar cycles 23, 24 and 25—including the 2024 Mother’s Day superstorm, we showcase how the latest GPPSn proton dataset (release v1.10), reprocessed and calibrated, can not only be used to monitor solar proton distributions inside the dynamic geomagnetic field for individual events, but also to derive a new empirical model linking cutoff L-shells with several key space weather parameters. This newly developed SEPCL-MEO model demonstrates high predictive performance; for example, predictions for > 30 MeV solar protons yield a correlation coefficient of 0.85 and performance efficiency of 0.67 when validated against GPPSn observations. Results from this pilot study underscores the scientific and operational value of the GPPSn dataset, and this dataset—when paired with machine-learning techniques—can play a critical role in observing and predicting the effects of future incoming SEP events, including extreme ones.
58 GEOSCIENCES↗
Scanning Mobility Particle Sizer, b1 level
AOS Scanning Mobility Particle Sizer data, b level.
54 ENVIRONMENTAL SCIENCES↗
Review of Particle Physics - Scalar Mesons below 1 GeV
The summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,717 new measurements from 869 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Most of the 120 reviews are updated, including many that are heavily revised. The is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings. The complete (both volumes) is published online on the website of the Particle Data Group () and in a journal. Volume 1 is available in print as the . A with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version optimized for use on phones, and as an Android app. The 2024 edition of the Review of Particle Physics should be cited as: S. Navas et al. (Particle Data Group), Phys. Rev. D 110, 030001 (2024)© 20242024
Navas, S. [Universidad de Granada]↗
Reinforcement Learning for Load-balanced Parallel Particle Tracing
We explore an online reinforcement learning (RL) paradigm to dynamically optimize parallel particle tracing performance in distributed-memory systems. Our method combines three novel components: (1) a work donation algorithm, (2) a high-order workload estimation model, and (3) a communication cost model. First, we design an RL-based work donation algorithm. Our algorithm monitors workloads of processes and creates RL agents to donate data blocks and particles from high-workload processes to low-workload processes to minimize program execution time. The agents learn the donation strategy on the fly based on reward and cost functions designed to consider processes' workload changes and data transfer costs of donation actions. Second, we propose a workload estimation model, helping RL agents estimate the workload distribution of processes in future computations. Third, we design a communication cost model that considers both block and particle data exchange costs, helping RL agents make effective decisions with minimized communication costs. We demonstrate that our algorithm adapts to different flow behaviors in large-scale fluid dynamics, ocean, and weather simulation data. Our algorithm improves parallel particle tracing performance in terms of parallel efficiency, load balance, and costs of I/O and communication for evaluations with up to 16,384 processors.
Distributed and parallel particle tracing↗
ExINP-ENA Ice-Nucleating Particle Concentrations - CRAFT
This data set comprises cumulative ambient ice-nucleating particle (INP) concentrations measured at the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) Eastern North Atlantic (ENA) observatory on Graciosa Island, Azores (39.0916° N, 28.0257° W). Our INP data were generated using a combination of online instruments, the Portable Ice Nucleation Experiment chamber (PINE), and an offline assay instrument, the West Texas Cryogenic Refrigerator Applied to Freezing Test system (WT-CRAFT), for aerosol particle samples collected on polycarbonate filters (with 0.2-micron diameter pores).
47-mm Nuclepore filter sampler, West Texas Cryogen↗
ExINP-ENA Ice-Nucleating Particle Concentrations - PINE
This data set comprises cumulative ambient ice-nucleating particle (INP) concentrations measured at the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) Eastern North Atlantic (ENA) observatory on Graciosa Island, Azores (39.0916° N, 28.0257° W). Our INP data were generated using a combination of online instruments, the Portable Ice Nucleation Experiment chamber (PINE), and an offline assay instrument, the West Texas Cryogenic Refrigerator Applied to Freezing Test system (WT-CRAFT), for aerosol particle samples collected on polycarbonate filters (with 0.2-micron diameter pores).
54 ENVIRONMENTAL SCIENCES↗