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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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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Particle accelerator spin-transparent storage rings for beyond state-of-the-art science

We describe spin-transparent storage rings that exhibit coherence times of many hours and store a large number of particles and their use in novel applications. For example, these rings can be used to directly measure the electric dipole moment of the electron, relevant to CP violation and matter-antimatter asymmetry in the universe, and to search for axion-mediated forces. These rings can also serve as a compelling platform for quantum computing. In particular, we will describe how spin-transparent rings can be used in conjunction with ion traps to enhance scalability and increase quantum-coherence times of ion quantum computing.

Suleiman, R.↗
DOE OSTI2024

Particle Accelerator Spin-Transparent Storage Rings for Beyond State-of-the-Art Science

We will describe spin-transparent storage rings that exhibit spin-coherence times of several hours and store a large number of particles and their use in novel applications. For example, these rings can be used to directly measure the electric dipole moment of the electron, relevant to CP violation and matter-antimatter asymmetry in the universe, and to search for dark energy and ultra-light dark matter*. These rings can also serve as a compelling platform for quantum computing. In this presentation, we will describe how spin-transparent rings can be used in conjunction with ion traps to enhance scalability and increase quantum coherence times of ion quantum computing.

Suleiman, R.↗
DOE OSTI2021

Scaling Inference Using Triton to Accelerate Particle Physics at the LHC and DUNE

DUNE and the LHC experiments consist of unique and cutting-edge particle detectors that create massive, complex, and rich datasets with billions of events. They require sophisticated algorithms to reconstruct and interpret the data. Modern machine learning algorithms provide a powerful toolset to detect and classify particles, from familiar image processing convolutional neural networks to newer graph neural network architectures. A full reconstruction of these particle collisions requires novel approaches to handle the computing challenge of processing so much raw data. In a series of studies, physicists from Fermilab, CERN, and university groups explored how to accelerate their data processing using the Triton Inference Server.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Transition to Petschek Reconnection in Subrelativistic Pair Plasmas: Implications for Particle Acceleration

While relativistic magnetic reconnection in pair plasmas has emerged in recent years as a candidate for the origin of radiation from extreme astrophysical environments, the corresponding subrelativistic pair-plasma regime has remained less explored, leaving open the question of how relativistic physics affects reconnection. In this paper, we investigate the differences between these regimes by contrasting two-dimensional particle-in-cell simulations of reconnection in pair plasmas with relativistic magnetization (σ ≫ 1) and subrelativistic magnetization (σ < 1). By utilizing unprecedentedly large domain sizes and outflow boundary conditions, we demonstrate that lowering the magnetization results in a change in the reconnection geometry from a plasmoid chain to a Petschek geometry, where laminar exhausts bounded by slow-mode shocks emanate from a single diffusion region. We attribute this change to the reduced plasmoid production rate in the low-σ case: When the secondary tearing rate is sufficiently low, plasmoids are too few in number to prevent the system from relaxing into a stable Petschek configuration. This geometric change also affects particle energization: We show that while high-σ plasmoid chains generate power-law energy spectra, low-σ Petschek exhausts merely heat incoming plasma and yield negligible nonthermal acceleration. These results have implications for predicting the global current sheet geometry and the resulting energy spectra in a variety of systems.

Plasma astrophysics↗
DOE OSTI2025

Particle acceleration during classical phase transitions on a spherical lattice

Abstract When compressed, certain lattices undergo phase transitions that may allow nuclei to gain significant kinetic energy. To explore the dynamics of this phenomenon, we develop a methodology to study Coulomb coupled N -body systems constrained to a sphere, as in the Thomson problem. We initialize N total Boron nuclei as point particles on the surface of the sphere, allowing them to equilibrate via Coulomb scattering with a viscous damping term. To simulate a phase transition, we remove N rm particles, forcing the system to rearrange into a new equilibrium. With this model, we consider the Thomson problem as a dynamical system, providing a framework to explore how non-zero temperature affects structural imperfections in Thomson minima. We develop a scaling relation for the average peak kinetic energy attained by a single particle as a function of N and N rm . For certain values of N , we find an order of magnitude energy gain when increasing N rm from 1 to 6. The model may help to design a lattice that maximizes the energy output.

Bachmann, Aidan M. (ORCID:0000000294706404)↗
DOE OSTI2023

Conceptual Design of a 20 T Hybrid Cos-Theta Di- pole Superconducting Magnet for Future High-Energy Particle Accelerators

High energy physics research will need more and more powerful circular accelerators in the next decades. It is therefore desirable to have dipole magnets able to produce the largest possible magnetic field, in order to keep the machine diameter within a reasonable size. A 20 T dipole is considered a desired achievement since it would allow the construction of an 80 km machine, able to circulate 100 TeV proton beams. In order to reach 20 T, a hybrid Low-Temperature Superconductor (LTS) - High-Temperature Superconductor (HTS) magnet is needed, since LTS technology is presently limited to ~16 T for accelerator magnet applications. In this paper, we present the design of a 6 layers 20 T hybrid dipole magnet using Nb3Sn (LTS) and Bi2212 (HTS). We show that it is possible to achieve this magnetic field with accelerator field quality, with sufficient margin on a realistic conductor, keeping the stresses within safe limit, avoiding conductor degradation

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Microchip accelerators

Particle accelerators are among the most important scientific tools of the modern age. Major accelerator facilities, such as the 27-km-circumference Large Hadron Collider in Switzerland, where the Higgs boson was recently discovered, allow scientists to uncover fundamental properties of matter and energy. But the particle energies needed to explore new regimes of physics have increased to the TeV scale and beyond, and accelerator facilities based on conventional technologies are becoming prohibitively large and costly. Further, even lower-energy, smaller-scale accelerators used in medicine and industry are often cumbersome devices; they can weigh several tons and cost millions of dollars.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

An adaptive approach to machine learning for compact particle accelerators

Abstract Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory, and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Evidence for flare-accelerated particles in large scale loops in the behind-the-limb gamma-ray solar flare of September 29, 2022

We report on the detection of the gamma-ray emission above 100 MeV from the solar flare of September 29, 2022, by Fermi LAT with simultaneous coverage in HXR by Solar Orbiter STIX. The Solar Orbiter-Earth separation was 178° at the time of the flare as seen from Earth, with Solar Orbiter observing the east limb. Based on STIX imaging, the flare was located 16° behind the eastern limb as seen from Earth. The STIX and GBM non-thermal emission and the LAT emission above 100 MeV all show similarly shaped time profiles, and the Fermi profiles peaked only 20 s after the STIX signal from the main flare site, setting this flare apart from all the other occulted flares observed by Fermi LAT. The radio spectral imaging based on the Nançay Radioheliograph and ORFEES spectrograph reveal geometries consistent with a magnetic structure that connects the parent active region behind the limb to the visible disk. We studied the basic characteristics of the gamma-ray time profile, in particular, the rise and decay times and the time delay between the gamma-ray and HXR peak fluxes. We compared the characteristics of this event with those of four Fermi LAT behind-the-limb flares and with an on-disk event and found that this event is strikingly similar to the impulsive on-disk flare. Based on multiwavelength observations, we find that the gamma-ray emission above 100 MeV originated from ions accelerated in the parent active region behind the limb and was transported to the visible disk via a large magnetic structure connected to the parent active region behind the limb. Our results strongly suggest that the source of the emission above 100 MeV from the September 29, 2022 flare cannot be the CME-driven shock.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Transversely pumped laser driven particle accelerator

We present a novel acceleration scheme capable of accelerating electrons and ions in an underdense plasma. Transversely Pumped Acceleration (TPA) uses multiple arrays of counter-propagating laser beamlets that focus onto a central acceleration axis. Tuning the injection timing and the spacing between the adjacent beamlets allows for precise control over the position and velocity of the intersection point of the counter-propagating beam arrays. This results in an accelerating structure that propagates orthogonal to the direction of laser propagation. We present the theory that sets the injection timing of the incoming pulses to accelerate electrons and ions with a tunable phase velocity plasma wave. Simulation results are also presented which demonstrate 1.12 GeV proton beams accelerated in 3.6 mm of plasma and electron acceleration gradients on the order of 1 TeV/m in a scheme that circumvents dephasing. This work has potential applications as a compact accelerator for medical physics and high energy physics colliders.

Laser-produced plasmas↗
DOE OSTI2022

Applications of machine learning to a compact magnetic spectrometer for high repetition rate, laser-driven particle acceleration

Accurately and rapidly diagnosing laser–plasma interactions is often difficult due to the time-intensive nature of the analysis and will only become more so with the rise of high repetition rate lasers and the desire to implement feedback on a commensurate timescale. Diagnostic analysis employing machine learning techniques can help address this problem while maintaining a high degree of accuracy. We report on the application of machine learning to the analysis of a scintillator-based electron spectrometer for experiments on high intensity, laser–plasma interactions at the Colorado State University Advanced Lasers and Extreme Photonics facility. Our approach utilizes a neural network trained on synthetic data and tested on experiments to extract the accelerated electron temperature. By leveraging transfer learning, we demonstrate an improvement in the neural network accuracy, decreasing the network error by 50%.

Swanson, K. K. (ORCID:0000000286880568)↗