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DOE OSTI2016

Neural Networks for Modeling and Control of Particle Accelerators

Particle accelerators are host to myriad nonlinear and complex physical phenomena. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. The purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Outlook towards deployable continual learning for particle accelerators

Particle accelerators are high power complex machines. To ensure uninterrupted operation of these machines, thousands of pieces of equipment need to be synchronized, which requires addressing many challenges including design, optimization and control, anomaly detection and machine protection. With recent advancements, machine learning (ML) holds promise to assist in more advance prognostics, optimization, and control. While ML based solutions have been developed for several applications in particle accelerators, only few have reached deployment and even fewer to long term usage, due to particle accelerator data distribution drifts caused by changes in both measurable and non-measurable parameters. In this paper, we identify some of the key areas within particle accelerators where continual learning can allow maintenance of ML model performance with distribution drifts. Particularly, we first discuss existing applications of ML in particle accelerators, and their limitations due to distribution drift. Next, we review existing continual learning techniques and investigate their potential applications to address data distribution drifts in accelerators. By identifying the opportunities and challenges in applying continual learning, this paper seeks to open up the new field and inspire more research efforts towards deployable continual learning for particle accelerators.

43 PARTICLE ACCELERATORS↗

DOE OSTI2018

Towards Cryogen-Free SRF Particle Accelerators

Particle accelerators have been essential tools of particle physics since initial development in the early twentieth century, with commercial applications soon following each successive advancement. Furthermore, particle accelerators today are found in diverse use, ranging from ion-implantation of consumer electronics to both imaging and treatment of cancers.

43 PARTICLE ACCELERATORS↗

DOE OSTI2026

Continual Learning for Particle Accelerators

Particle accelerators operate under dynamically changing conditions, which often lead to data distribution drifts. These drifts pose significant challenges for Machine Learning (ML) models, which typically fail to maintain performance when faced with such non-stationary data. In particle accelerators, the primary sources of these data drifts include changes in accelerator settings and non-measured parameters such as machine degradation and environmental factors. Previous research has proposed conditional models to handle multiple beam configurations effectively; however, it is challenging to train the ML models on all possible configuration settings. Additionally, conditional models alone can not address performance degradation caused by drifts due to non-measured factors. These limitations contribute to a significant gap between ML development and its deployment in real-world operational settings. To bridge this gap, in this paper, we identify some of the key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. In addition, we present a practical use case where a conditional Auto-Encoder model coupled with memory-based continual learning has been employed to demonstrate stable performance even when underlying data drifts.

Schram, Malachi [Thomas Jefferson National Acceler↗

DOE OSTI2026

Continual Learning for Particle Accelerators

Rajput, Kishansingh [Thomas Jefferson National Acc↗

DOE OSTI2016

Magnetic Materials Characterization and Modeling for the Enhanced Design of Magnetic Shielding of Cryomodules in Particle Accelerators

Particle accelerators produce beams of high-energy particles, which are used for both fundamental and applied scientific research and are critical to the development of accelerator driven sub-critical reactor systems. An effective magnetic shield is very important to achieve higher quality factor (Qo) of the cryomodule of a particle accelerator. The allowed value of field inside the cavity due to all external fields (particularly the Earth’s magnetic field) is ~15 mG or less. The goal of this PhD dissertation is to comprehensively study the magnetic properties of commonly used magnetic shielding materials at both cryogenic and room temperatures. This knowledge can be used for the enhanced design of magnetic shields of cryomodes (CM) in particle accelerators. To this end, we first studied the temperature dependent magnetization behavior (M-H curves) of Amumetal and A4K under different annealing and deformation conditions. This characterized the effect of stress or deformation induced during the manufacturing processes and subsequent restoration of high permeability with appropriate heat treatment. Next, an energy based stochastic model for temperature dependent anhysteretic magnetization behavior of ferromagnetic materials was proposed and benchmarked against experimental data. We show that this model is able to simulate and explain the magnetic behavior of as rolled, deformed and annealed amumetal and A4K over a large range of temperatures. The experimental results for permeability are then used in a finite element model (FEM) in COMSOL to evaluate the shielding effectiveness of multiple shield designs at room temperature as well as cryogenic temperature. This work could serve as a guideline for future design, development and fabrication of magnetic shields of CMs.

Sah, Sanjay↗

DOE OSTI2026

Continual Learning for Production-Level Machine Learning in Particle Accelerators

Particle accelerators operate in complex environments where data distribution can change dynamically, leading to data drifts that significantly challenge Machine Learning (ML) models. These non-stationary conditions often cause ML models to deteriorate in performance, making it difficult to maintain reliable predictions in operation. The primary sources of data drifts are changes in accelerator settings and changes in equipment performance which cannot be measured directly. To bridge this gap between ML development and long-term deployment in operational settings, we identify key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. We will provide a practical guide on selecting the appropriate method given resource constraints and desired stability plasticity trade offs. As a concrete example, we will present a real-world use case for anomaly detection to predict errant beams at the Spallation Neutron Source accelerator, where continual learning has been employed to demonstrate stable performance on drifting data streams. We will present practical challenges, lessons learned, and the results from the deployed ML model.

Rajput, Kishansingh [Thomas Jefferson National Acc↗

DOE OSTI2023

Overview of H- radio frequency ion sources for particle accelerators

Particle accelerators are among the most important scientific tools of the modern era. Large accelerator complexes have supported scientific user facilities which have had an enormous societal impact spanning many decades and enabling the work of thousands of scientific users worldwide contributing to many Nobel prizes in physics, biology and chemistry [1]. Many of the large hadron facilities employ accelerator complexes which include cyclotrons, synchrotrons, storage rings, linear or tandem accelerators and deliver ion beams of very high-intensity and/or very high-energy to their user facilities. These accelerator complexes require the injection of high-intensity beams of ions which are produced within an ion source and formed within a plasma or by bombardment of a surface [2]. Increasingly, RF systems are being utilized, to generate these ion-rich plasmas due to their high reliability and minimal use of consumable components. This report will first discuss the basic mechanisms of ion formation and plasma generation as well as some specifics of RF/microwave generators, matching circuits and plasma coupling structures typically employed. A detailed discussion will then be given of the RF-driven negative ion source systems employed by US Spallation Neutron Source as well as those used in similar facilities located around the globe. REFERENCES [1]Vladimir Shiltsev, “Particle beams behind physics discoveries”, Physics Today 73, Issue 4, 32 (2020) [2]Robert Welton et al, “Negative hydrogen ion sources for particle accelerators: Sustainability issues and recent improvements in long-term operations”, Journal of Physics Conf Series, 2244 012045

Welton, Robert F.↗

DOE OSTI2020

On-chip integrated laser-driven particle accelerator

Particle accelerators represent an indispensable tool in science and industry. However, the size and cost of conventional radio-frequency accelerators limit the utility and reach of this technology. Dielectric laser accelerators (DLAs) provide a compact and cost-effective solution to this problem by driving accelerator nanostructures with visible or near-infrared pulsed lasers, resulting in a 10 4 reduction of scale. Current implementations of DLAs rely on free-space lasers directly incident on the accelerating structures, limiting the scalability and integrability of this technology. In this article, we present an experimental demonstration of a waveguide-integrated DLA that was designed using a photonic inverse-design approach. By comparing the measured electron energy spectra with particle-tracking simulations, we infer a maximum energy gain of 0.915 kilo–electron volts over 30 micrometers, corresponding to an acceleration gradient of 30.5 mega–electron volts per meter. On-chip acceleration provides the possibility for a completely integrated mega–electron volt-scale DLA.

43 PARTICLE ACCELERATORS↗

DOE OSTI2016

SMALL-SCALE MAGNETIC ISLANDS IN THE SOLAR WIND AND THEIR ROLE IN PARTICLE ACCELERATION. II. PARTICLE ENERGIZATION INSIDE MAGNETICALLY CONFINED CAVITIES

We explore the role of heliospheric magnetic field configurations and conditions that favor the generation and confinement of small-scale magnetic islands associated with atypical energetic particle events (AEPEs) in the solar wind. Some AEPEs do not align with standard particle acceleration mechanisms, such as flare-related or simple diffusive shock acceleration processes related to interplanetary coronal mass ejections (ICMEs) and corotating interaction regions (CIRs). As we have shown recently, energetic particle flux enhancements may well originate locally and can be explained by particle acceleration in regions filled with small-scale magnetic islands with a typical width of ∼0.01 au or less, which is often observed near the heliospheric current sheet (HCS). The particle energization is a consequence of magnetic reconnection-related processes in islands experiencing either merging or contraction, observed, for example, in HCS ripples. Here we provide more observations that support the idea and the theory of particle energization produced by small-scale-flux-rope dynamics (Zank et al. and Le Roux et al.). If the particles are pre-accelerated to keV energies via classical mechanisms, they may be additionally accelerated up to 1–1.5 MeV inside magnetically confined cavities of various origins. The magnetic cavities, formed by current sheets, may occur at the interface of different streams such as CIRs and ICMEs or ICMEs and coronal hole flows. They may also form during the HCS interaction with interplanetary shocks (ISs) or CIRs/ICMEs. Particle acceleration inside magnetic cavities may explain puzzling AEPEs occurring far beyond ISs, within ICMEs, before approaching CIRs as well as between CIRs.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

The Effect of Inverse Compton Losses on Particle Acceleration in Three-dimensional Relativistic Reconnection

Relativistic magnetic reconnection is a key mechanism for dissipating magnetic energy and accelerating particles in astrophysics. In the absence of radiative cooling, recent particle-in-cell (PIC) simulations have shown that high-energy particles gain most of their energy in the upstream region, during a short-lived “free phase” where they meander between the two sides of the layer; when they get captured/trapped by the downstream flux ropes, they undergo a “trapped phase,” where no significant energization occurs. Here, we perform a suite of 3D PIC simulations of relativistic reconnection, including inverse Compton (IC) losses in the weakly cooled regime in which the radiation-reaction-limited Lorentz factor γrad exceeds the magnetization σ. We show that electron cooling losses do not appreciably alter the reconnection rate, the structure of the layer, and the physics of particle acceleration in the free phase, so the spectrum of free electrons is dN free /dγ ∝ γ −1 , as in the uncooled case. The spectrum of trapped electrons above the cooling break γcool (in the range γ cool < γ < γ rad ) is dN/dγ ∝ γ −3 , steeper than the scaling dN/dγ ∝ γ −2 of uncooled simulations. This confirms that no significant particle energization occurs during the trapped phase. Our results validate the model by Zhang et al. for particle acceleration in 3D relativistic reconnection, and imply that radiative emission models of reconnection-powered astrophysical sources should employ a two-zone structure that differentiates between free, rapidly accelerating particles and trapped, passively cooling particles.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

The effect of inverse Compton losses on particle acceleration in three-dimensional relativistic reconnection

Relativistic magnetic reconnection is a key mechanism for dissipating magnetic energy and accelerating particles in astrophysics. In the absence of radiative cooling, recent particle-in-cell (PIC) simulations have shown that high-energy particles gain most of their energy in the upstream region, during a short-lived "free phase" where they meander between the two sides of the layer; when they get captured/trapped by the downstream flux ropes, they undergo a "trapped phase", where no significant energization occurs. Here, we perform a suite of 3D PIC simulations of relativistic reconnection including inverse Compton (IC) losses in the weakly cooled regime in which the radiation-reaction-limited Lorentz factor $γ_{\rm rad}$ exceeds the magnetization $σ$. We show that electron cooling losses do not appreciably alter the reconnection rate, the structure of the layer, and the physics of particle acceleration in the free phase, so the spectrum of free electrons is $dN_{\rm free}/dγ\propto γ^{-1}$, as in the uncooled case. The spectrum of trapped electrons above the cooling break $γ_{\rm cool}$ (in the range $γ_{\rm cool}<γ<γ_{\rm rad}$) is $dN/dγ\propto γ^{-3}$, steeper than the scaling $dN/dγ\propto γ^{-2}$ of uncooled simulations. This confirms that no significant particle energization occurs during the trapped phase. Our results validate the model by arXiv:2302.12269 for particle acceleration in 3D relativistic reconnection, and imply that radiative emission models of reconnection-powered astrophysical sources should employ a two-zone structure, that differentiates between free, rapidly accelerating particles and trapped, passively cooling particles.

FOS: Physical sciences↗

DOE OSTI2017

Non-thermal particle acceleration in collisionless relativistic electron–proton reconnection

Magnetic reconnection in relativistic collisionless plasmas can accelerate particles and power high-energy emission in various astrophysical systems. Whereas most previous studies focused on relativistic reconnection in pair plasmas, less attention has been paid to electron–ion plasma reconnection, expected in black hole accretion flows and relativistic jets. In this paper, we report a comprehensive particle-in-cell numerical investigation of reconnection in an electron–ion plasma, spanning a wide range of ambient ion magnetizations σi, from the semirelativistic regime (ultrarelativistic electrons but non-relativistic ions, 10 -3 &NestedLessLess; σ i &NestedLessLess; 1) to the fully relativistic regime (both species are ultrarelativistic, σi &NestedGreaterGreater; 1). We investigate how the reconnection rate, electron and ion plasma flows, electric and magnetic field structures, electron/ion energy partitioning, and non-thermal particle acceleration depend on σi. Our key findings are: (1) the reconnection rate is about 0.1 of the Alfvénic rate across all regimes; (2) electrons can form concentrated moderately relativistic outflows even in the semirelativistic, small-σi regime; (3) while the released magnetic energy is partitioned equally between electrons and ions in the ultrarelativistic limit, the electron energy fraction declines gradually with decreased σi and asymptotes to about 0.25 in the semirelativistic regime; and (4) reconnection leads to efficient non-thermal electron acceleration with a σi-dependent power-law index, p (σ i )≃const + 0.7σ i -1/2 . Finally, these findings are important for understanding black hole systems and lend support to semirelativistic reconnection models for powering non-thermal emission in blazar jets, offering a natural explanation for the spectral indices observed in these systems.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Effect of magnetic field inclination on black hole jet power and particle acceleration

Rotating black holes are known to launch relativistic jets and accelerate particles if they accrete a magnetized plasma. It remains unclear, however, how the global magnetic field orientation affects the jet powering efficiency. We propose the first kinetic study of a collisionless plasma around a Kerr black hole embedded in a magnetic field that is inclined with respect to the black hole spin axis. Using three-dimensional general relativistic particle-in-cell simulations, we show that while oblique magnetic field configurations significantly reduce the jet power, particle acceleration still remains highly efficient. This suggests that black holes producing a weak jet might still be bright sources of nonthermal radiation and cosmic rays.

acceleration of particles↗

DOE OSTI2013

A Chandra view of non-thermal emission in the northwestern region of supernova remnant RCW 86: Particle acceleration and magnetic fields

The shocks of supernova remnants are believed to accelerate particles to cosmic ray (CR) energies. The amplification of the magnetic field due to CRs propagating in the shock region is expected to have an impact on both the emission from the accelerated particle population as well as the acceleration process itself. Using a 95 ks observation with the Advanced CCD Imaging Spectrometer on board the Chandra X-Ray Observatory, we map and characterize the synchrotron emitting material in the northwest region of RCW 86. We model spectra from several different regions, both filamentary and diffuse, where emission appears to be dominated by synchrotron radiation. The fine spatial resolution of Chandra allows us to obtain accurate emission profiles across three different non-thermal rims in this region. The narrow width (l ≈ 10''-30'') of these filaments constrains the minimum magnetic field strength at the post-shock region to approximately 80 μG.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2013

COMBINING PARTICLE ACCELERATION AND CORONAL HEATING VIA DATA-CONSTRAINED CALCULATIONS OF NANOFLARES IN CORONAL LOOPS

We model nanoflare heating of extrapolated active-region coronal loops via the acceleration of electrons and protons in Harris-type current sheets. The kinetic energy of the accelerated particles is estimated using semi-analytical and test-particle-tracing approaches. Vector magnetograms and photospheric Doppler velocity maps of NOAA active region 09114, recorded by the Imaging Vector Magnetograph, were used for this analysis. A current-free field extrapolation of the active-region corona was first constructed. The corresponding Poynting fluxes at the footpoints of 5000 extrapolated coronal loops were then calculated. Assuming that reconnecting current sheets develop along these loops, we utilized previous results to estimate the kinetic energy gain of the accelerated particles. We related this energy to nanoflare heating and macroscopic loop characteristics. Kinetic energies of 0.1-8 keV (for electrons) and 0.3-470 keV (for protons) were found to cause heating rates ranging from 10{sup -6} to 1 erg s{sup -1} cm{sup -3}. Hydrodynamic simulations show that such heating rates can sustain plasma in coronal conditions inside the loops and generate plasma thermal distributions that are consistent with active-region observations. We concluded the analysis by computing the form of X-ray spectra generated by the accelerated electrons using the thick-target approach. These spectra were found to be in agreement with observed X-ray spectra, thus supporting the plausibility of our nanoflare-heating scenario.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

A critical shock mach number for particle acceleration in the absence of pre-existing cosmic rays: M=√5

It is shown that, under some generic assumptions, shocks cannot accelerate particles unless the overall shock Mach number exceeds a critical value M>√5. The reason is that for M≤√5 the work done to compress the flow in a particle precursor requires more enthalpy flux than the system can sustain. This lower limit applies to situations without significant magnetic field pressure. In case that the magnetic field pressure dominates the pressure in the unshocked medium, i.e., for low plasma beta, the resistivity of the magnetic field makes it even more difficult to fulfill the energetic requirements for the formation of shock with an accelerated particle precursor and associated compression of the upstream plasma. We illustrate the effects of magnetic fields for the extreme situation of a purely perpendicular magnetic field configuration with plasma beta β = 0, which gives a minimum Mach number of M = 5/2. The situation becomes more complex, if we incorporate the effects of pre-existing cosmic rays, indicating that the additional degree of freedom allows for less strict Mach number limits on acceleration. We discuss the implications of this result for low Mach number shock acceleration as found in solar system shocks, and shocks in clusters of galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Nonthermal Particle Acceleration in 3D Relativistic Magnetic Reconnection in Pair Plasma

As a fundamental process converting magnetic to plasma energy in high-energy astrophysical plasmas, relativistic magnetic reconnection is a leading explanation for the acceleration of particles to the ultrarelativistic energies that are necessary to power nonthermal emission (especially X-rays and gamma-rays) in pulsar magnetospheres and pulsar wind nebulae, coronae and jets of accreting black holes, and gamma-ray bursts. An important objective of plasma astrophysics is therefore the characterization of nonthermal particle acceleration (NTPA) effected by reconnection. Reconnection-powered NTPA has been demonstrated over a wide range of physical conditions using large 2D kinetic simulations. However, its robustness in realistic 3D reconnection—in particular, whether the 3D relativistic drift-kink instability (RDKI) disrupts NTPA—has not been systematically investigated, although pioneering 3D simulations have observed NTPA in isolated cases. Here, we present the first comprehensive study of NTPA in 3D relativistic reconnection in collisionless electron–positron plasmas, characterizing NTPA as the strength of 3D effects is varied systematically via the length in the third dimension and the strength of the guide magnetic field. We find that, while the RDKI prominently perturbs 3D reconnecting current sheets, it does not suppress particle acceleration, even for zero guide field; fully 3D reconnection robustly and efficiently produces nonthermal power-law particle spectra closely resembling those obtained in 2D. This finding provides strong support for reconnection as the key mechanism powering high-energy flares in various astrophysical systems. We also show that strong guide fields significantly inhibit NTPA, slowing reconnection and limiting the energy available for plasma energization, yielding steeper and shorter power-law spectra.

79 ASTRONOMY AND ASTROPHYSICS↗