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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 19 records
All sourcesNASA NTRSDOE OSTI
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 OSTI2024

Conditional guided generative diffusion for particle accelerator beam diagnostics

Abstract Advanced accelerator-based light sources such as free electron lasers (FEL) accelerate highly relativistic electron beams to generate incredibly short (10s of femtoseconds) coherent flashes of light for dynamic imaging, whose brightness exceeds that of traditional synchrotron-based light sources by orders of magnitude. FEL operation requires precise control of the shape and energy of the extremely short electron bunches whose characteristics directly translate into the properties of the produced light. Control of short intense beams is difficult due to beam characteristics drifting with time and complex collective effects such as space charge and coherent synchrotron radiation. Detailed diagnostics of beam properties are therefore essential for precise beam control. Such measurements typically rely on a destructive approach based on a combination of a transverse deflecting resonant cavity followed by a dipole magnet in order to measure a beam’s 2D time vs energy longitudinal phase-space distribution. In this paper, we develop a non-invasive virtual diagnostic of an electron beam’s longitudinal phase space at megapixel resolution (1024 × 1024) based on a generative conditional diffusion model. We demonstrate the model’s generative ability on experimental data from the European X-ray FEL.

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 OSTI2024

Low Energy Muon Beam Diagnostics: Scintillating Fiber Profile Monitor

The MeV Test Area (MTA) houses the 400 MeV H- Beam at the end of the Linac and a secondary beamline of muons and pions created from hitting a Tungsten target. The MTA is in the Irradiation Test Area (ITA), where experiments involve studying the effects of radiation on materials in the MTA beam. There is a need for a retractable detector to monitor the secondary beam’s intensity and coarse profile. The SFPM was chosen when considering factors like ability to measure low rates and handle high intensities. The plan was to test different scintillating fibers & Silicon Photomultiplier (SiPM) models to figure out which combination gives the best signals, and gain familiarity with the detector assembly, operation, and data acquisition software before using with final detectors in beamline. The SiPM circuit has been tested with a LED pulser successfully. The immediate steps that follow will be to set the SiPM up on the monitor with three different types of optical fibers, use the Caen software to configure the best mode of data acquisition, and proceed with an offline source test using Strontium-90. Once the most efficient measurement method is clear, they will be ready for use in the beamline.

Leith, Justin↗
DOE OSTI2024

Low Energy Muon Beam Diagnostics - Scintillating Fiber Profile Monitor (SFPM)

The MeV Test Area (MTA) houses the 400 MeV H^− Beam at the end of the Linac and a secondary beamline of muons and pions created from hitting a Tungsten target. The MTA is in the Irradiation Test Area (ITA), where experiments involve studying the effects of radiation on materials in the MTA beam. There is a need for a retractable detector to monitor the secondary beam s intensity and coarse profile. The SFPM was chosen when considering factors like ability to measure low rates and handle high intensities. The plan was to test different scintillating fibers & Silicon Photomultiplier (SiPM) models to figure out which combination gives the best signals, and gain familiarity with the detector assembly, operation, and data acquisition software before using with final detectors in beamline. The SiPM circuit has been tested with a LED pulser successfully. The immediate steps that follow will be to set the SiPM up on the monitor with three different types of optical fibers, use the Caen software to configure the best mode of data acquisition, and proceed with an offline source test using Strontium-90. Once the most efficient measurement method is clear, they will be ready for use in the beamline.

Leith, Justin↗
DOE OSTI2024

Recent progress in laser wire-based H- beam diagnostics at the SNS linac

Laser wire has been used for nonintrusive profile and emittance measurements of operational hydrogen ion (H-) beam at the SNS linac. In this talk, we will describe the following recent developments in the laser wire system. 1) An upgraded light source and laser transport line which enables novel measurement capabilities including longitudinal profile measurement and high-energy proton beam extraction over potentially an entire macropulse. 2) A dual-detector emittance measurement scheme that boosted the dynamic range by an order of magnitude. 3) Design and implementation laser-wire-based nonintrusive longitudinal phase space measurement system.

Liu, Yun↗
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↗
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↗