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At least 19 records

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

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

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

Rajput, Kishansingh [Thomas Jefferson National Acc

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

Jupiter radio bursts and particle acceleration

Particle acceleration processes are important in understanding many of the Jovian radio and plasma wave emissions. However, except for the high-energy electrons that generate synchrotron emission following inward diffusion from the outer magnetosphere, acceleration processes in Jupiter's magnetosphere and between Jupiter and Io are poorly understood. We discuss very recent observations from the Ulysses spacecraft of two new Jovian radio and plamas wave emissions in which particle acceleration processes are important and have been addressed directly by complementary investigations. First, radio bursts known as quasi-periodic bursts have been observed in close association with a population of highly energetic electrons. Second, a population of much lower energy (keV range) electrons on auroral field lines can be shown to be responsible for the first observation of a Jovian plasma wave emission known as auroral hiss.

Desch, Michael D.

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

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

Neutron and gamma-ray signatures for particle acceleration in solar flares

The production of neutrons, 4 to 7 MeW nuclear gamma rays, positrons and pions resulting from the interaction of flare accelerated particles with the solar atmosphere was calculated. For the energy spectra of these particles the Bessel function predicted by stochastic acceleration and power laws which could result from acceleration at large-scale planar shocks was used, with the thick-target model. It is assumed that in the interaction region the accelerated particles are isotropic. The neutron-to-2.223 MeV photon conversion factors for various flare locations on the Sun are derived by averaging conversion factors over these spectra. Comparing calculations with data confirms that for most gamma-ray flares the bulk of the accelerated particles remain trapped at the Sun and that these particles have spectra similar to the spectra of flare particles observed in interplanetary space.

Ramaty, R.

Particle Acceleration and Associated Emission from Relativistic Shocks

Five talks consist of a research program consisting of numerical simulations and theoretical development designed to provide an understanding of the emission from accelerated particles in relativistic shocks. The goal of this lecture is to discuss the particle acceleration, magnetic field generation, and radiation along with the microphysics of the shock process in a self-consistent manner. The discussion involves the collisionless shocks that produce emission from gamma-ray bursts and their afterglows, and producing emission from supernova remnants and AGN relativistic jets. Recent particle-in-cell simulation studies have shown that the Weibel (mixed mode two-stream filamentation) instability is responsible for particle (electron, positron, and ion) acceleration and magnetic field generation in relativistic collisionless shocks. 3-D RPIC code parallelized with MPI has been used to investigate the dynamics of collisionless shocks in electron-ion and electron-positron plasmas with and without initial ambient magnetic fields. In this lecture we will present brief tutorials of RPIC simulations and RMHD simulations, a brief summary of recent RPIC simulations, mechanisms of particle acceleration in relativistic shocks, and calculation of synchrotron radiation by tracing particles. We will discuss on emission from the collisionless shocks, which will be calculated during the simulation by tracing particle acceleration self-consistently in the inhomogeneous magnetic fields generated in the shocks. In particular, we will discuss the differences between standard synchrotron radiation and the jitter radiation that arises in turbulent magnetic fields.

Nishkawa, Ken-Ichi

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

Studies of Particle Acceleration, Transport and Radiation in Impulsive Phase of Solar Flares

Solar activity and its most prominent aspect, the solar flares, have considerable influence on terrestrial and space weather. Solar flares also provide a suitable laboratory for the investigation of many plasma and high energy processes important in the magnetosphere of the Earth and many other space and astrophysical situations. Hence, progress in understanding of flares will have considerable scientific and societal impact. The primary goal of this grant is the understanding of two of the most important problems of solar flare physics, namely the determination of the energy release mechanism and how this energy accelerates particles. This is done through comparison of the observations with theoretical models, starting from observations and gradually proceeding to theoretically more complex situations as the lower foundations of our understanding are secured. It is generally agreed that the source of the flare energy is the annihilation of magnetic fields by the reconnection process. Exactly how this energy is released or how it is dissipated remains controversial. Moreover, the exact mechanism of the acceleration of the particles is still a matter of debate. Data from many spacecrafts and ground based instruments obtained over the past decades have given us some clues. Theoretical analyses of these data have led to the standard thick target model (STT) where most of the released energy goes into an (assumed) power law spectrum of accelerated particles, and where all the observed radiations are the consequence of the interaction of these particles with the flare plasma. However, some theoretical arguments, and more importantly some new observations, have led us to believe that the above picture is not complete. It appears that plasma turbulence plays a more prominent role than suspected previously, and that it is the most likely agent for accelerating particles. The model we have developed is based on production of a high level of plasma waves and turbulence in the reconnection region above a flare loop. This turbulence accelerates particles stochastically which radiate some of their energy in this region but carry most of their energy to the footpoints of the loop, where they lose all their energy and radiate bulk of the observed radiation as in the traditional thick target model. In the past we have worked on various aspects of this model. We have evaluated the interaction rates of the plasma waves with electrons and ions, developed theoretical frameworks for the acceleration, transport and radiative processes, and produced numerical codes for the investigation of these processes. The goal of this grant has been further development and testing of this new paradigm, with emphases on the relative acceleration of electrons and ions and on a comprehensive investigation of the turbulence generation, cascade, and damping processes. We review several pieces of important evidence that we have uncovered indicating the crucial roles of turbulence, in and we describe accomplishments during the past two years of this grant.

Petrosian, Vahe

Microscopic Processes On Radiation from Accelerated Particles in Relativistic Jets

Nonthermal radiation observed from astrophysical systems containing relativistic jets and shocks, e.g., gamma-ray bursts (GRBs), active galactic nuclei (AGNs), and Galactic microquasar systems usually have power-law emission spectra. Recent PIC simulations of relativistic electron-ion (electro-positron) jets injected into a stationary medium show that particle acceleration occurs within the downstream jet. In the collisionless relativistic shock particle acceleration is due to plasma waves and their associated instabilities (e.g., the Buneman instability, other two-streaming instability, and the Weibel (filamentation) instability) created in the shocks are responsible for particle (electron, positron, and ion) acceleration. The simulation results show that the Weibel instability is responsible for generating and amplifying highly nonuniform, small-scale magnetic fields. These magnetic fields contribute to the electron's transverse deflection behind the jet head. The jitter'' radiation from deflected electrons has different properties than synchrotron radiation which is calculated in a uniform magnetic field. This jitter radiation may be important to understanding the complex time evolution and/or spectral structure in gamma-ray bursts, relativistic jets, and supernova remnants.

Nishikawa, K.-I.

Radiation from Shock-Accelerated Particles

Plasma instabilities excited in collisionless shocks are responsible for particle acceleration, generation of magnetic fields , and associated radiation. We have investigated the particle acceleration and shock structure associated with an unmagnetized relativistic jet propagating into an unmagnetized plasma. Cold jet electrons are thermalized and slowed while the ambient electrons are swept up to create a partially developed hydrodynamic-like shock structure. The shock structure depends on the composition of the jet and ambient plasma (electron-positron or electron-ions). Strong electromagnetic fields are generated in the reverse , jet shock and provide an emission site. These magnetic fields contribute to the electron's transverse deflection behind the shock. 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 detailed properties of the radiation are important for understanding the complex time evolution and/or spectral structure in gamma-ray bursts, relativistic jet shocks, and supernova remnants

Nishikawa, Ken-ichi

Solar gamma-ray lines as probes of accelerated particle directionalities in flares

Anisotropies of charged particles accelerated in solar flares were studied by observing Doppler shifts of selected gamma-ray lines. The spectral shape was calculated of the 6.1-MeV line of O-16. If the accelerated particles are isotropic, the line remains centered at e sub 0 = 6129.4 keV, and its width (FWHM) is about 100 keV. For particle anisotropies that may be produced in solar flares, the line is shifted to lower energies by about 30 to 40 keV.

Ramaty, R.

Solar gamma-ray lines as probes of accelerated particle directionalities in flares

Anisotropies of charged particles accelerated in solar flares can be studied by observing Doppler shifts of selected gamma-ray lines. The spectral shape of the 6.1-MeV line of O-16 is calculated. If the accelerated particles are isotropic, the line remains centered at an emitting-nucleus rest-frame energy of 6129.4 keV, and its width (FWHM) is about 100 keV. However, for particle anisotropies that may be produced in solar flares, the line is shifted to lower energies by about 30 to 40 keV.

Ramaty, R.

Modeling of Particle Acceleration at Multiple Shocks Via Diffusive Shock Acceleration: Preliminary Results

We present preliminary results from a model that diffusively accelerates particles at multiple shocks. Our basic approach is related to box models (Protheroe and Stanev, 1998; Moraal and Axford, 1983; Ball and Kirk, 1992; Drury et al., 1999) in which a distribution of particles is diffusively accelerated inside the box while simultaneously experiencing decompression through adiabatic expansion and losses from the convection and diffusion of particles outside the box (Melrose and Pope, 1993; Zank et al., 2000). We adiabatically decompress the accelerated particle distribution between each shock by either the method explored in Melrose and Pope (1993) and Pope and Melrose (1994) or by the approach set forth in Zank et al. (2000) where we solve the transport equation by a method analogous to operator splitting. The second method incorporates the additional loss terms of convection and diffusion and allows for the use of a variable time between shocks. We use a maximum injection energy (Emax) appropriate for quasi-parallel and quasi-perpendicular shocks (Zank et al., 2000, 2006; Dosch and Shalchi, 2010) and provide a preliminary application of the diffusive acceleration of particles by multiple shocks with frequencies appropriate for solar maximum (i.e., a non-Markovian process).

Parker, Linda Neergaard

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