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Dataset results
21 results for “acceleration of particles”
6D phase space of charged beam in particle accelerator
<p>The dataset is collected from HPSim (https://github.com/apphys/hpsim), an advanced, open-source tool developed at LANL, enables rapid, online simulations of multipleparticle beam dynamics is used to collect data. HPSim solves Vlasov-Maxwell equations to calculate the effects of external accelerating and focusing forces on the charged particle beam as well as space charge forces within the beam. To generate the dataset from HPSim, the RF set points (amplitude and phase) for the first four modules are randomly sampled from a uniform distribution keeping the rest of the set points of 44 modules at a mean value. Other beam and accelerator parameters, like the initial beam condition, are also set to constant realistic values. Using the RF set points as inputs to the simulation, HPSim provides a six-dimensional phase space of the charged particle beam in the form of 15 unique projections at each of the 48 accelerating section/modules of LANSCE linear accelerator. </p>
Accurate modeling of plasma acceleration with arbitrary order pseudo-spectral particle-in-cell methods
<p>This is supplementary material to the publication "Accurate modeling of plasma acceleration with arbitrary order pseudo-spectral particle-in-cell methods" by S. Jalas et. al.</p> <p>Particle in Cell (PIC) simulations are a widely used tool for the investigation of both laser- and beam-driven plasma acceleration. It is a known issue that the beam quality can be artificially degraded by numerical Cherenkov radiation (NCR) resulting primarily from an incorrectly modeled dispersion relation. Pseudo- spectral solvers featuring infinite order stencils can strongly reduce NCR – or even suppress it – and are therefore well suited to correctly model the beam properties. For efficient parallelization of the PIC algorithm, however, localized solvers are inevitable. Arbitrary order pseudo-spectral methods provide this needed locality. Yet, these methods can again be prone to NCR. Here, we show that acceptably low solver orders are sufficient to correctly model the physics of interest, while allowing for parallel computation by domain decomposition.</p> <p>The given script can be run with the open source particle in cell codes FBPIC (https://github.com/fbpic/fbpic) and Warp (https://bitbucket.org/berkeleylab/warp)</p> <p>The produced data is conformant with the openPMD standard (openpmd.org) and can be analysed for example with the openPMD-viewer (https://github.com/openPMD/openPMD-viewer).</p>
Data Archive: 2021 Development of a Virtual Diagnostic for the Advanced Particle Accelerator Modeling Code WarpX
<p><strong>A current promising field of research, laser-driven ion acceleration has the potential to reduce the size, cost, and energy consumption of particle accelerators by orders of magnitude.</strong></p> <p> </p> <p><strong>To better refine the instrumentation, we have developed a virtual diagnostic to measure electromagnetic radiation such as radiation produced from scattered and transmitted laser beams which has been implemented into WarpX, an advanced Particle-in-Cell code that simulates laser-driven particle acceleration. This “FieldProbe” diagnostic provides field measurements and is parallelized using the Message Passing Interface (MPI) and can thus run on High Performance Computing systems such as the NERSC Cori cluster.</strong></p>
Supplementary materials: "Synthesizing Particle-in-Cell Simulations Through Learning and GPU Computing for Hybrid Particle Accelerator Beamlines"
<p>Data archive for stage 3 of manuscript for PASC24. See Readme stage 3.txt for more details.</p> <p> </p> <p>This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231 and by LLNL under Contract DE-AC52-07NA27344. This material is based upon work supported by the U.S. Department of Energy, OFfice of Science, Office of High Energy Physics, General Accelerator R&D (GARD), under contract number DE-AC02-05CH11231. This material is based upon work supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. This research was supported by the Exascale Computing Project (17-SC-20-SC), a joint project of the U.S. Department of Energy's Office of Science and National Nuclear Security Administration, responsible for delivering a capable exascale ecosystem, including software, applications, and hardware technology, to support the nation's exascale computing imperative.This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0023719.</p>
Large Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Language
<p>Autonomous tuning of particle accelerators is an active and challenging field of research with the goal of enabling novel accelerator technologies cutting-edge high-impact applications, such as physics discovery, cancer research and material sciences. A key challenge with autonomous accelerator tuning remains that the most capable algorithms require an expert in optimisation, machine learning or a similar field to implement the algorithm for every new tuning task. In this work, we propose the use of large language models (LLMs) to tune particle accelerators. We demonstrate on a proof-of-principle example the ability of LLMs to successfully and autonomously tune a particle accelerator subsystem based on nothing more than a natural language prompt from the operator, and compare the performance of our LLM-based solution to state-of-the-art optimisation algorithms, such as Bayesian optimisation (BO) and reinforcement learning-trained optimisation (RLO). In doing so, we also show how LLMs can perform numerical optimisation of a highly non-linear real-world objective function. Ultimately, this work represents yet another complex task that LLMs are capable of solving and promises to help accelerate the deployment of autonomous tuning algorithms to the day-to-day operations of particle accelerators.</p>
Experimantal data related to " Electron phase space control in on-chip laser-driven particle acceleration "
<p>Experimental and Simualtion data used to generate the Plots in the manusript of "Electron phase space control in on-chip laser-driven particle acceleration". Use Matlab file (R2019a or later) to generte plots.</p>
Emulator of PR-DNS: Accelerating Dynamical Fields with Neural Operators in Particle-Resolved Direct Numerical Simulation
<p>The codes directory includes the various machine learning models, such as FNO, UNet and ResNet. R128_init1 and R128_init2 are the PR-DNS time step simulations at different initial conditions. R64_init2, R128_init2 and R256_init2 are the PR-DNS time step simulations at different resolutions. </p> <p> </p>
Improved hybrid particle swarm optimizer with sine-cosine acceleration coefficients for transient electromagnetic inversion
<p>The Xishan Landslide Research area is located in Sichuan Lixian County, Southwest China. </p> <p>The N.O. 1 profile from Li et al., (2020), containing 15 measuring points, was chosen to test data fitting of IH-PSO-SCAC and to determine the hydrogeological structure of landslides.</p> <p> </p>
[Dataset] Intensity and Dimensionality-Dependent Dynamics of Laser-Proton Acceleration in 1D, 2D, and 3D Particle-in-Cell Simulations
<p>The included Jupyter Notebooks (.ipynb) were used to generate figures and make calculations for the work. The resulting image files are saved in Figure_Outputs.tar.gz. The other archives (1D.tar.gz, 2DS.tar.gz, 2DP.tar.gz, 3D.tar.gz, laser_focus.tar.gz, and EField.tar.gz) are compressed directories containing EPOCH PIC simulation outputs for the paper (relevant directories should be extracted to replicate analysis with provided files). </p> <p>Since the last version, a new figure (Figure 6, an ion phase space) has been added. Notebooks for later figures have been renumbered and the figure outputs have been updated. </p>
Supplementary Materials: Next Generation Computational Tools for the Modeling and Design of Particle Accelerators at Exascale
<p>Supplementary materials (aka data artifact or data archive) for our NAPAC22 publication: "Next Generation Computational Tools for the Modeling and Design of Particle Accelerators at Exascale" (Paper ID: TUYE2).</p> <p>Work supported by the Exascale Computing Project (17-SC-20-SC), a joint project of the U.S. Department of Energy's Office of Science and National Nuclear Security Administration, responsible for delivering a capable exascale ecosystem, including software, applications, and hardware technology, to support the nation's exascale computing imperative. This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231.<br> This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231.</p>
Improved hybrid particle swarm optimizer with sine-cosine acceleration coefficients for transient electromagnetic inversion(2)
<p>The datas are gained in<Nonlinear programming genetic algorithm in transient electromagnetic inversion>. Li F P, Yang H Y, Liu X H, et al.</p> <p>Geophysical and Geochemical Exploration,2017,41(2) :347-353.</p> <p>http://doi.org /10.11720/wtyht.2017.2.24</p>
Wind Energetic Particle Acceleration, Composition and Transport (EPACT)/LEMT 1-Hr Omnidirectional Fluxes
This dataset contains hourly-averaged omnidirectional fluxes for He, C, O, Ne, Si, and Fe in particles/cm2-sr-s-MeV/nuc in seven energy bins (six for Ne) in the general range of 2-10 MeV/nucleon. The error bars are statistical only, that is, corresponding to sqrt(N), where N is the number of collected ions. For intervals in which zero ions were observed, the error bar corresponds to one ion. ASCII listing generated from this link provide the start-time of the averaging interval as YYYY DD HH, followed by pairs of numbers giving the flux and its uncertainty from the selected intervals. The available energy ranges for bins bby species are:+----------------------------------------------------------------------------------------------+| Species | Band 1 | Band 2 | Band 3 | Band 4 | Band 5 | Band 6 | Band 7 ||----------------------------------------------------------------------------------------------|| He | 2.00-2.40 | 2.40-3.00 | 3.00-3.70 | 3.70-4.53 | 4.53-6.00 | 6.00-7.40 | 7.40-9.64 || C | 2.57-3.19 | 3.19-3.85 | 3.85-4.80 | 4.80-5.80 | 5.80-7.20 | 7.20-9.10 | 9.10-13.70 || O | 2.56-3.17 | 3.17-3.88 | 3.88-4.68 | 4.68-6.00 | 6.00-7.40 | 7.40-9.20 | 9.20-13.40 || Ne | 3.27-3.98 | 3.98-4.72 | 4.72-5.92 | 5.92-7.87 | 7.87-9.96 | 9.96-12.7 | || Si | 2.50-3.20 | 3.20-4.00 | 4.00-4.90 | 4.90-6.00 | 6.00-7.90 | 7.90-9.70 | 9.70-13.60 || Fe | 2.40-3.00 | 3.00-3.95 | 3.95-4.80 | 4.80-5.90 | 5.90-7.80 | 7.80-9.30 | 9.30-12.50 |+----------------------------------------------------------------------------------------------+
Wind Energetic Particle Acceleration, Composition and Transport (EPACT)/LEMT 5-min Omnidirectional Fluxes
This dataset contains 5-min omnidirectional fluxes for He, C, O, Ne, Si, and Fe in particles/cm2-sr-s-MeV/nuc in seven energy bins (six for Ne) in the general range of 2-10 MeV/nucleon. The error bars are statistical only, that is, corresponding to sqrt(N), where N is the number of collected ions. For intervals in which zero ions were observed, the error bar corresponds to one ion. ASCII listing generated from this link provide the start-time of the averaging interval as YYYY DD HH, followed by pairs of numbers giving the flux and its uncertainty from the selected intervals. The available energy bins for the species are:+----------------------------------------------------------------------------------------------+| Species | Band 1 | Band 2 | Band 3 | Band 4 | Band 5 | Band 6 | Band 7 ||----------------------------------------------------------------------------------------------|| He | 2.00-2.40 | 2.40-3.00 | 3.00-3.70 | 3.70-4.53 | 4.53-6.00 | 6.00-7.40 | 7.40-9.64 || C | 2.57-3.19 | 3.19-3.85 | 3.85-4.80 | 4.80-5.80 | 5.80-7.20 | 7.20-9.10 | 9.10-13.70 || O | 2.56-3.17 | 3.17-3.88 | 3.88-4.68 | 4.68-6.00 | 6.00-7.40 | 7.40-9.20 | 9.20-13.40 || Ne | 3.27-3.98 | 3.98-4.72 | 4.72-5.92 | 5.92-7.87 | 7.87-9.96 | 9.96-12.7 | || Si | 2.50-3.20 | 3.20-4.00 | 4.00-4.90 | 4.90-6.00 | 6.00-7.90 | 7.90-9.70 | 9.70-13.60 || Fe | 2.40-3.00 | 3.00-3.95 | 3.95-4.80 | 4.80-5.90 | 5.90-7.80 | 7.80-9.30 | 9.30-12.50 |+----------------------------------------------------------------------------------------------+
Diesel exhaust particles Enhance Monocyte Recruitment, Accelerating Allergic Airway Inflammation
GEO Series GSE283909. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Wind Energetic Particle Acceleration Composition Transport (EPACT) SupraThermal Energetic Particle Telescope (STEP) Differential, Directional Hydrogen Fluxes, 10 min Data
The EPACT Instrument on Wind STEP - SupraThermal Energetic Particle Telescope measures Ion Fluxes of Protons (H) in 0.12.5 MeV Energy Range and He-Fe Nuclei in the ~0.032 MeV/nucleon Energy Ranges in two identical Telescopes, each with a Geometrical Factor of 0.4 cm^2 sr and a rectangular Field of View with an Angular Acceptance of 44° in Azimuth and 17° in Polar Angle.
Wind Energetic Particle Acceleration Composition Transport (EPACT) SupraThermal Energetic Particle Telescope (STEP) Differential, Directional Iron Fluxes, 10 min Data
The EPACT Instrument on Wind STEP - SupraThermal Energetic Particle Telescope measures Ion Fluxes of Protons (H) in 0.12.5 MeV Energy Range and He-Fe Nuclei in the ~0.032 MeV/nucleon Energy Ranges in two identical Telescopes, each with a Geometrical Factor of 0.4 cm^2 sr and a rectangular Field of View with an Angular Acceptance of 44° in Azimuth and 17° in Polar Angle.
Wind Energetic Particle Acceleration, Composition and Transport (EPACT) APE-B Telescope Proton Fluxes 18.90 to 21.90 MeV, Level 2, 1 hr Data
The Energetic Particles: Acceleration, Composition and Transport, EPACT, on Wind. The APE-B Telescope measures Proton Fluxes at Particle Energies of 18.90 MeV to 21.90 MeV.
Wind Energetic Particle Acceleration, Composition and Transport (EPACT) 92s KP Fluxes
This set of key parameter flux data from the Wind EPACT (Energetic Particles: Acceleration, Composition and Transport) telescopes contains fluxes of protons (19-72 MeV in 2 energy bins), He nuclei (0.08-72 MeV/n in 5 bins), CNO nuclei (80-640 MeV/n in 2 bins), O nuclei (3.2-6.2 MeV/n in 1 bin), Fe nuclei (0.08-6.2 MeV/n in 3 bins) and 1-10 MeV electrons, each averaged over 92 seconds.
Wind Energetic Particle Acceleration Composition Transport (EPACT) SupraThermal Energetic Particle Telescope (STEP) Differential, Directional Carbon, Nitrogen, and Oxygen Fluxes, 10 min Data
The EPACT Instrument on Wind STEP - SupraThermal Energetic Particle Telescope measures Ion Fluxes of Protons (H) in 0.12.5 MeV Energy Range and He-Fe Nuclei in the ~0.032 MeV/nucleon Energy Ranges in two identical Telescopes, each with a Geometrical Factor of 0.4 cm^2 sr and a rectangular Field of View with an Angular Acceptance of 44° in Azimuth and 17° in Polar Angle.
Wind Energetic Particle Acceleration Composition Transport (EPACT) SupraThermal Energetic Particle Telescope (STEP) Differential, Directional Helium Fluxes, 10 min Data
The EPACT Instrument on Wind STEP - SupraThermal Energetic Particle Telescope measures Ion Fluxes of Protons (H) in 0.12.5 MeV Energy Range and He-Fe Nuclei in the ~0.032 MeV/nucleon Energy Ranges in two identical Telescopes, each with a Geometrical Factor of 0.4 cm^2 sr and a rectangular Field of View with an Angular Acceptance of 44° in Azimuth and 17° in Polar Angle.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.