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DOE OSTI2024

Towards Agentic AI on Particle Accelerators

As particle accelerators grow in complexity, traditional control methods face increasing challenges in achieving optimal performance. This paper envisions a paradigm shift: a decentralized multi-agent framework for accelerator control, powered by Large Language Models (LLMs) and distributed among autonomous agents. We present a proposition of a self-improving decentralized system where intelligent agents handle high-level tasks and communication and each agent is specialized control individual accelerator components. This approach raises some questions: What are the future applications of AI in particle accelerators? How can we implement an autonomous complex system such as a particle accelerator where agents gradually improve through experience and human feedback? What are the implications of integrating a human-in-the-loop component for labeling operational data and providing expert guidance? We show two examples, where we demonstrate viability of such architecture.

43 PARTICLE ACCELERATORS↗

NASA NTRS1987

Nuclear processes and accelerated particles in solar flares

Nuclear processes and particle acceleration in solar flares are discussed and the theory of gamma-ray and neutron production is reviewed. Gamma-ray, neutron, and charged-particle observations of solar flares are compared with predictions, and the implications of these comparisons for particle energy spectra, total numbers, anisotropies, electron-to-proton ratios, and acceleration mechanisms are considered. Elemental and isotopic abundances of the ambient gas derived from gamma-ray observations have also been compared to abundances obtained from observations of escaping accelerated particles and other sources.

Ramaty, R.↗

DOE OSTI2024

Robust errant beam prognostics with conditional modeling for particle accelerators

Abstract Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, they can fault and abort operations for numerous reasons, lowering efficiency and science output. To avoid these faults, we apply anomaly detection techniques to predict unusual behavior and perform preemptive actions to improve the total availability. Supervised machine learning (ML) techniques such as siamese neural network models can outperform the often-used unsupervised or semi-supervised approaches for anomaly detection by leveraging the label information. One of the challenges specific to anomaly detection for particle accelerators is the data’s variability due to accelerator configuration changes within a production run of several months. ML models fail at providing accurate predictions when data changes due to changes in the configuration. To address this challenge, we include the configuration settings into our models and training to improve the results. Beam configurations are used as a conditional input for the model to learn any cross-correlation between the data from different conditions and retain its performance. We employ conditional siamese neural network (CSNN) models and conditional variational auto encoder (CVAE) models to predict errant beam pulses at the spallation neutron source under different system configurations and compare their performance. We demonstrate that CSNNs outperform CVAEs in our application.

43 PARTICLE ACCELERATORS↗

NASA NTRS1987

Nuclear processes and accelerated particles in solar flares

Nuclear processes and particle acceleration in solar flares are reviewed. The theory of gamma-ray and neutron production is discussed and results of calculations are compared to gamma-ray, neutron, and charged-particle observations from solar flares. The implications of these comparisons on particle energy spectra, total numbers, anisotropies, electron-to-proton ratios, as well as on acceleration mechanisms and the interaction site, are presented. The information on elemental and isotopic abundances derived from gamma-ray observations is compared to abundances obtained from escaping accelerated particles and other sources.

Ramaty, R.↗

DOE OSTI2025

Harnessing the power of gradient-based simulations for multi-objective optimization in particle accelerators

Abstract Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-objective optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, such as genetic algorithms (GAs), have been leveraged for many optimization problems, however, they do not apply to complex control problems by design. This paper demonstrates the power of differentiability for solving MOO problems in particle accelerators using a deep differentiable reinforcement learning (DDRL) algorithm. We compare the DDRL algorithm with model-free reinforcement learning (MFRL), GA, and Bayesian optimization (BO) for simultaneous optimization of heat load and trip rates in the continuous electron beam accelerator facility. The underlying problem enforces strict constraints on both individual states and actions as well as cumulative (global) constraints on energy requirements of the beam. Using historical accelerator data, we develop a physics-based surrogate model which is differentiable and allows for back-propagation of gradients. The results are evaluated in the form of a Pareto-front with two objectives. We show that the DDRL outperforms MFRL, BO, and GA on high dimensional problems.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Explainable and Differentiable Reinforcement Learning for Multi-objective Optimization in Particle Accelerators

Operating particle accelerators involves optimizing multiple goals simultaneously, which can be challenging due to trade-offs among objectives. While evolutionary algorithms like the genetic algorithm (GA) have been used for various Multi-Objective Optimization (MOO) tasks, they are not inherently suited for complex control problems. This talk highlights two variations of Reinforcement Learning (RL) for concurrently optimizing heat load and trip rates at the Continuous Electron Beam Accelerator Facility (CEBAF). The problem involves strict constraints on individual states, actions, and overall energy requirements of the beam. First, this talk highlights how differentiability can be harnessed through a Deep Differentiable Reinforcement Learning (DDRL) approach to address MOO issues within particle accelerators. We examine the DDRL method alongside Model Free Reinforcement Learning (MFRL), GA, and Bayesian Optimization (BO). The performance of these methods is assessed by generating a Pareto-front for two objectives. Our findings indicate that DDRL excels in handling high-dimensional problems more effectively than MFRL, BO, and GA. Next, we will show integration of explainable physics-based constraints into RL algorithms to enhance trans- parency and trust in decision-making processes by enabling users to verify that agents adhere to established physical principles. This surrogate function can be modeled using neural networks or sparse dictionary mod- els. By examining the mathematical form of the learned constraint function, we are able to confirm the agent has learned to use the established physics of each environment provided but the surrogate model. In addi- tion, we find that the introduction of a mathematical functional dictionary based surrogate model enables our reinforcement learning algorithms to reliably converge for difficult high-dimensional accelerator controls environments.

Rajput, Kishansingh [Thomas Jefferson National Acc↗

DOE OSTI2025

Leveraging prior mean models for faster Bayesian optimization of particle accelerators

Tuning particle accelerators is a challenging and time-consuming task that can be automated and carried out efficiently using suitable optimization algorithms, such as model-based Bayesian optimization techniques. One of the major advantages of Bayesian algorithms is the ability to incorporate prior information about beam physics and historical behavior into the model used to make control decisions. In this work, we examine incorporating prior accelerator physics information into Bayesian optimization algorithms by utilizing fast executing, neural network models trained on simulated or historical datasets as prior mean functions in Gaussian process models. We show that in ideal cases, this technique substantially increases convergence speed to optimal solutions in high-dimensional tuning parameter spaces. Additionally, we demonstrate that even in non-ideal cases, where prior models of beam dynamics do not exactly match experimental conditions, the use of this technique can still enhance convergence speed. Finally, we demonstrate how these methods can be used to improve optimization in practical applications, such as transferring information gained from beam dynamics simulations to online control of the LCLS injector, and transferring knowledge gained from experimental measurements across different operating modes, such as accelerating different ion species at the ATLAS heavy ion accelerator.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Graph learning for particle accelerator operations

Particle accelerators play a crucial role in scientific research, enabling the study of fundamental physics and materials science, as well as having important medical applications. This study proposes a novel graph learning approach to classify operational beamline configurations as good or bad. By considering the relationships among beamline elements, we transform data from components into a heterogeneous graph. We propose to learn from historical, unlabeled data via our self-supervised training strategy along with fine-tuning on a smaller, labeled dataset. Additionally, we extract a low-dimensional representation from each configuration that can be visualized in two dimensions. Leveraging our ability for classification, we map out regions of the low-dimensional latent space characterized by good and bad configurations, which in turn can provide valuable feedback to operators. This research demonstrates a paradigm shift in how complex, many-dimensional data from beamlines can be analyzed and leveraged for accelerator operations.

43 PARTICLE ACCELERATORS↗

NASA NTRS2015

Analytic Method to Estimate Particle Acceleration in Flux Ropes

The mechanism that accelerates particles to the energies required to produce the observed high-energy emission in solar flares is not well understood. Drake et al. (2006) proposed a kinetic mechanism for accelerating electrons in contracting magnetic islands formed by reconnection. In this model, particles that gyrate around magnetic field lines transit from island to island, increasing their energy by Fermi acceleration in those islands that are contracting. Based on these ideas, we present an analytic model to estimate the energy gain of particles orbiting around field lines inside a flux rope (2.5D magnetic island). We calculate the change in the velocity of the particles as the flux rope evolves in time. The method assumes a simple profile for the magnetic field of the evolving island; it can be applied to any case where flux ropes are formed. In our case, the flux-rope evolution is obtained from our recent high-resolution, compressible 2.5D MHD simulations of breakout eruptive flares. The simulations allow us to resolve in detail the generation and evolution of large-scale flux ropes as a result of sporadic and patchy reconnection in the flare current sheet. Our results show that the initial energy of particles can be increased by 2-5 times in a typical contracting island, before the island reconnects with the underlying arcade. Therefore, particles need to transit only from 3-7 islands to increase their energies by two orders of magnitude. These macroscopic regions, filled with a large number of particles, may explain the large observed rates of energetic electron production in flares. We conclude that this mechanism is a promising candidate for electron acceleration in flares, but further research is needed to extend our results to 3D flare conditions.

Energetic particle acceleration↗

NASA NTRS2016

Magnetic-Island Contraction and Particle Acceleration in Simulated Eruptive Solar Flares

The mechanism that accelerates particles to the energies required to produce the observed high-energy impulsive emission in solar flares is not well understood. Drake et al. proposed a mechanism for accelerating electrons in contracting magnetic islands formed by kinetic reconnection in multi-layered current sheets (CSs). We apply these ideas to sunward-moving flux ropes (2.5D magnetic islands) formed during fast reconnection in a simulated eruptive flare. A simple analytic model is used to calculate the energy gain of particles orbiting the field lines of the contracting magnetic islands in our ultrahigh-resolution 2.5D numerical simulation. We find that the estimated energy gains in a single island range up to a factor of five. This is higher than that found by Drake et al. for islands in the terrestrial magnetosphere and at the heliopause, due to strong plasma compression that occurs at the flare CS. In order to increase their energy by two orders of magnitude and plausibly account for the observed high-energy flare emission, the electrons must visit multiple contracting islands. This mechanism should produce sporadic emission because island formation is intermittent. Moreover, a large number of particles could be accelerated in each magneto hydro dynamic-scale island, which may explain the inferred rates of energetic-electron production in flares. We conclude that island contraction in the flare CS is a promising candidate for electron acceleration in solar eruptions.

Guidoni, S. E.↗

NASA NTRS1975

Observations of Jovian accelerated particles both inside and outside the Jovian magnetosphere - Results from the Goddard-U. of New Hampshire experiment on Pioneer 10

This paper discusses measurements of the energetic-particle population inside Jupiter's magnetosphere as well as observations of energetic particles in interplanetary space that have escaped from that magnetosphere. The discussion is based on Pioneer 10 data obtained when the spacecraft was outside the bow shock but within about 1 AU of the planet and on electron data obtained near earth by various IMP spacecraft. Pioneer 10 particle measurements in the Jovian magnetosphere are reviewed, the differential proton and electron energy spectra are described, and it is suggested that very little particle acceleration occurs in the outer magnetosphere. Additional data are examined in regard to Jovian alpha particles, proton and electron angular distributions, protons in the inner magnetosphere, effects of Io on the proton flux, electron acceleration outside the bow shock, and observations of Jovian electrons near earth. All these data are shown to support the conclusion that most, if not all, quiet-time increases in low-energy electron fluxes near earth are of Jovian origin.

Trainor, J. H.↗

DOE OSTI2023

Parallel Simulation of Beam Dynamics in Particle Accelerators [Slides]

Particle accelerators are among the most versatile and important tools of scientific discovery. The Nation's accelerators are responsible for a wealth of advances in materials science, chemistry, the biosciences, particle physics, and nuclear physics. They also have important applications to national security, the environment, energy, medicine, and on the quality of people's lives. LANL has a long history of making pioneering contributions to Accelerator Science including key contributions to the field of Computational Accelerator Physics. These include the development of early beam dynamics codes with space charge (such as PARMILA and PARMELA), the development of rf cavity codes and magnet codes (including Poisson and Superfish), and the development and distribution of codes to the accelerator community through the Los Alamos Accelerator Code Group. LANL researchers also helped pioneer the development of massively parallel space-charge codes. In project t22_accelsim we have moved beyond electrostatic models of collective effects (i.e., solving the Poisson equation in the bunch frame) to fully electromagnetic models based on the Lienard-Wiechert formalism. This approach enables the large-scale simulation of radiation production and collective effects in high brightness electron beams. This is highly relevant to LANL given its future goal of developing an X-ray Free Electron Laser (XFEL). It also directly impacts a LANL LDRD project to develop an undulator-based non-invasive beam profile monitor for beams created in laser-plasma accelerator systems.

43 PARTICLE ACCELERATORS↗

NASA NTRS1982

Cosmic-ray acceleration by stellar winds. II - The spectrum of accelerated particles

Consideration is given to the spectrum of particles accelerated at a stellar wind terminal shock and, at the same time, modulated by convection and diffusion in the stellar wind and decelerated by adiabatic expansion of the unshocked wind. It is noted that a proper study of this problem combines the whole of the cosmic-ray modulation problem with first-order Fermi acceleration at the shock due to repeated diffusive scattering across the shock. The modulation aspect of the model described here is much simpler than modern models of modulation alone; this makes it possible to derive illustrative analytical expressions for the accelerated spectra. Particular cases of monoenergetic and truncated power laws are computed. Energy losses and convective modulation compete with acceleration at the shock so effectively in this model that enhancement of the background cosmic-ray flux by a factor of more than 2 is extremely unlikely.

Forman, M. A.↗

NASA NTRS1986

Investigations of turbulent motions and particle acceleration in solar flares

Investigations of X-raya spectra of solar flares show that intense random (turbulent) motions are present in hot flare plasma. Here it is argued that the turbulent motions are of great importance for flare development. They can efficiently enhance flare energy release and accelerate particles to high energies.

Jakimiec, J.↗

NASA NTRS1990

Particle acceleration by intense auroral VLF turbulence

Broadband turbulence in the lower-hybrid to plasma frequency range is found in a variety of forms in the suprauroral region, most notably as auroral hiss and VLF saucers. When the turbulence is intense, it is observed to be associated with ion conics (ions heated transverse to the geomagnetic field) and 'counter-streaming' electron fluxes (heated in both directions parallel to the field). A review is presented of the dispersion and propagation characteristics of whistler resonance-cone waves, which comprise the turbulence. Plasma simulation and mesoscale (Monte Carlo) simulation techniques are used to illustrate the interaction of the ambient plasma with the turbulence. These calculations demonstrate how this interaction results in transverse heating of the ions and parallel heating of the electrons of the plasma, leading to the formation of the observed heated and accelerated particle fluxes.

Retterer, John M.↗

NASA NTRS2012

Radiation from Accelerated Particles in Shocks and Reconnections

We have investigated particle acceleration and shock structure associated with an unmagnetized relativistic jets propagating into an unmagnetized plasmas. Strong magnetic fields generated in the trailing shock contribute to the electrons transverse deflection and acceleration. We have calculated, self-consistently, the radiation from electrons accelerated in the turbulent magnetic fields. We found that the synthetic spectra depend on the Lorentz factor of the jet, its thermal temperature and strength of the generated magnetic fields. The properties of the radiation may be important for understanding the complex time evolution and/or spectral structure in gamma-ray bursts, relativistic jets in general, and supernova remnants

Nishikawa, K.-I.↗

DOE OSTI2023

Modified split structure particle accelerators

A particle accelerator can include a first waveguide portion and a second waveguide portion. The first waveguide portion can include a first plurality of cell portions and a first iris portion that is disposed between two of the first plurality of cell portions. The first iris portion can include a first portion of an aperture such that the aperture is configured to be disposed about a beam axis. The first waveguide portion can further include a first bonding surface. The second waveguide portion can include a second plurality of cell portions and a second iris portion that is disposed between two of the second plurality of cell portions. The second iris portion can include a second portion of the aperture. The second waveguide portion can include a second bonding surface.

Agustsson, Ronald↗

DOE OSTI2023

Modified split structure particle accelerators

Agustsson, Ronald↗