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Results for “Beam diagnostics”

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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1966

Electron beam diagnostics.

Spectroscopic electron beam diagnostic technique, examining negligible effect elevated vibrational temperatures have on measured rotational temperatures of molecular nitrogen

ELECTRON BEAM↗
NASA NTRS1965

Investigation of ion beam diagnostics final report

Ion beam diagnostics - plasma wind tunnel stream generation and characteristics, mercury optical resonance probe, environment effects on ion stream neutralization, and electric field meters

PLASMA GENERATION↗
DOE OSTI2026

AI-driven Neutrino Beam Diagnostics for Next-Generation Neutrino Experiments

The accelerator-driven beam uncertainty limits oscillation measurements in long-baseline neutrino experiments. Spill-resolved beam diagnostics and real-time inference are necessary to address these neutrino flux systematics. As such, we present a machine-learning-based beam monitoring framework developed and validated using data from the T2K experiment. Our approach uses downstream, spill-by-spill muon monitor observables to predict upstream parameters such as proton beam position and width. We achieve high predictive accuracy on nominal runs, demonstrating robust baseline performance whether the model is trained on stable runs or systematically varied conditions. The framework is designed to be robust against domain shifts, allowing the neural network architectures and inference strategies developed with T2K data to be retrained and validated using LBNF simulations, with the goal of eventual deployment under real LBNF/DUNE operating conditions. This scalable approach to real-time beam inference offers a pathway toward reducing flux systematics for next-generation neutrino experiments such as DUNE.

Aney, Noah [Fermilab; U. Chicago (main)]↗
DOE OSTI2025

Precision beam diagnostics at the NuMI facility using muon monitor observations

The Neutrinos at the Main Injector (NuMI) facility at Fermilab delivers an intense neutrino beam for multiple experiments by producing pions that decay into neutrinos, muons, and other particles. Magnetic horns—the primary pion focusing elements in the NuMI beamline—exhibit predominantly linear optics, enabling a predictable relationship between the proton beam and the resulting pion and muon phase spaces. This study has two primary objectives: first, to evaluate and confirm the linearity of the horn focusing mechanism using analytical models and numerical simulations; and second, to demonstrate that key beam parameters—such as proton beam intensity, beam position on target, and horn current—can be extracted from muon monitor observations within this linear optics framework. Using a machine learning model trained on spill-by-spill muon monitor data, we infer the horn current with a precision of ±0.05%, the beam intensity with ±0.1%, and the beam position on target with ±0.018⁢ mm horizontally and ±0.013⁢ mm vertically. This approach provides a reliable cross-check of beam parameters, helping to reduce systematic uncertainties that are critical for future experiments such as the Deep Underground Neutrino Experiment, which will rely on the neutrino beam produced by the Long-Baseline Neutrino Facility.

Beam control↗
DOE OSTI2025

On Possibilities of Using Edge Radiation for X-ray and Electron Beam Diagnostics at Bending Magnet Beamlines of NSLS-II

Spectral flux density distributions of long-wavelength Edge Radiation, i.e. radiation generated by relativistic electrons at edges of bending magnets in a storage ring are described, both analytically and numerically. Possibilities of using this radiation for the alignment of optical components of bending magnet beamlines at NSLS-II and for non-destructive X-ray and electron beam diagnostics at these beamlines are discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Deployment and validation of predictive 6-dimensional beam diagnostics through generative reconstruction with standard accelerator elements

Understanding the 6-dimensional phase space distribution of particle beams is essential for optimizing accelerator performance. Conventional diagnostics such as use of transverse deflecting cavities offer detailed characterization but require dedicated hardware and space. Generative phase space reconstruction (GPSR) methods have shown promise in beam diagnostics, yet prior implementations still rely on such components. Here we present the first experimental implementation and validation of the GPSR methodology, realized by the use of standard accelerator elements including accelerating cavities and dipole magnets, to achieve complete 6-dimensional phase space reconstruction. Through simulations and experiments at the Pohang Accelerator Laboratory X-ray Free Electron Laser facility, we successfully reconstruct complex, nonlinear beam structures. Furthermore, we validate the methodology by predicting independent downstream measurements excluded from training, revealing the reconstruction closely resembling ground truth. This advancement establishes a pathway for predictive diagnostics across beamline segments while reducing hardware requirements and expanding applicability to various accelerator facilities.

Kim, Seongyeol [Pohang Univ. of Science and Techno↗
DOE OSTI2025

Machine Learning for LBNF Beam Diagnostics

This paper focuses on developing a machine learning model for predicting initial beam parameters for the Long Baseline Neutrino Facility (LBNF) beamline using downstream muon monitor data. Parameters such as proton beam position on target, sigma on target, focusing horn current, and focusing horn tilt are parameters we anticipate to be predictable based on the muon monitors. Uncertainty in initial beam condition measurements are a major contributor to uncertainty in downstream flux, and over operation time beam misalignment can occur [1]. A machine learning model has promise to detect anomalies along the beamline based on discrepancies between predicted configurations and measured configurations, and thus can expedite error detection and handling. A PyTorch neural network is defined, trained, and tested. The developed model currently does not provide reliable predictions, with the lowest loss being 0.09.. Further steps to improve the model’s accuracy are discussed, as well as future plans to detect anomalous beam conditions using a digital twin.

O'Brien, Bridget [Fermilab]↗
DOE OSTI2025

Development Of Non-Invasive Beam Diagnostics By Quantum Optics-Based Detection

We report our effort and latest progress developing diagnostics using quantum optics-based detection method for determining the spatial properties and current of electron beams. As electrons pass through a dilute vapor of rubidium atoms, their magnetic field perturb the atomic spin’s quantum state and causes polarization rotation of a laser resonant with an optical transition of the atoms. By measuring the polarization rotation angle across the laser beam, we recreate a 2D projection of the magnetic field and use it to determine the e-beam position, size and total current. We tested this method for an e-beam with currents ranging from 30 to 110 µA. Our study shows this approach is insensitive to electron kinetic energy, and is initially verified experimentally between 10 to 20 keV. A different approach was also performed with highly excited rubidium atoms in Rydberg states through a coherent two photon process. We use this detection scheme to measure the electric field generated by a

Zhang, S. [Thomas Jefferson National Accelerator F↗
NASA NTRS1994

Electron-Beam Diagnostic Methods for Hypersonic Flow Diagnostics

The purpose of this work was the evaluation of the use of electron-bean fluorescence for flow measurements during hypersonic flight. Both analytical and numerical models were developed in this investigation to evaluate quantitatively flow field imaging concepts based upon the electron beam fluorescence technique for use in flight research and wind tunnel applications. Specific models were developed for: (1) fluorescence excitation/emission for nitrogen, (2) rotational fluorescence spectrum for nitrogen, (3) single and multiple scattering of electrons in a variable density medium, (4) spatial and spectral distribution of fluorescence, (5) measurement of rotational temperature and density, (6) optical filter design for fluorescence imaging, and (7) temperature accuracy and signal acquisition time requirements. Application of these models to a typical hypersonic wind tunnel flow is presented. In particular, the capability of simulating the fluorescence resulting from electron impact ionization in a variable density nitrogen or air flow provides the capability to evaluate the design of imaging instruments for flow field mapping. The result of this analysis is a recommendation that quantitative measurements of hypersonic flow fields using electron-bean fluorescence is a tractable method with electron beam energies of 100 keV. With lower electron energies, electron scattering increases with significant beam divergence which makes quantitative imaging difficult. The potential application of the analytical and numerical models developed in this work is in the design of a flow field imaging instrument for use in hypersonic wind tunnels or onboard a flight research vehicle.

Source record↗
DOE OSTI2026

AI-Ready Control System for the Fermilab Accelerator Complex

Reliable, high-intensity operation of the Fermilab Accelerator Complex is critical to the success of the Long-Baseline Neutrino Facility and Deep Underground Neutrino Experiment. We describe the requirements and infrastructure necessary to support routine use of artificial intelligence and machine learning (AI/ML) in the accelerator control system. Three capabilities are identified: a machine learning operations (MLOps) framework standardizing the lifecycle of AI/ML automation from data management through deployment and monitoring; a data quality framework defining and enforcing standards required to build trustworthy AI/ML applications; and workflow integration with large language models to assist physicists, engineers, and operators with information retrieval, code development, and routine analysis. Use cases spanning beam diagnostics, beam control, and support system automation illustrate the technical requirements across the complex.

43 PARTICLE ACCELERATORS↗