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Fermilab’s Proton Improvement Plan II will power the long-term future of the U.S. accelerator-based particle physics program and enable the world’s most powerful high-energy neutrino beamfor the international Deep Underground Neutrino Experiment.
Fermilab’s Proton Improvement Plan II will power the long-term future of the U.S. accelerator-based particle physics program and enable the world’s most powerful high-energy neutrino beam for the international Deep Underground Neutrino Experiment.
Particle acceleration by evaporation pressure using Q-switched laser beam
Radio evidence for nonthermal particle acceleration on stars of late spectral type (G, K, and M) is reviewed, including the pre-main-sequence T Tauri stars, the dwarf M flare stars, and the RS CVn binary stars. Both the long-lasting radio emission and the transient radio flares can be attributed to nonthermal electrons accelerated in magnetically confined coronae, radiating by either incoherent gyrosynchrotron radiation or by coherent processes. Related observations of the Sun provide the framework for interpreting the radio emission of late-type stars. We review recent Very Long Base Interferometry (VLBI) observations of single and binary stars, observations of rapid acceleration and dynamic spectra of dwarf M flare stars, and models for long-lasting radio emission from dwarf M flare stars and RS CVn stars. The VLBI data provide evidence for nonthermal particle acceleration in large-scale magnetospheres several times larger than the stellar radius for T Tauri and RS CVn stars. In contrast, rapid radio flares on dwarf M stars require sources much smaller than the visible star in size, and this is consistent with the VLBI data. Dynamic spectra suggest particle acceleration by relativistic electron beams on dwarf M stars. The long-lived radio radiation of dwarf M stars and RS CVn stars requires more-or-less continuous particle acceleration over periods of hours, perhaps by persistent low-amplitude flaring.
Nonthermal radiation observed from astrophysical systems containing (relativistic) jets and shocks, e.g., supernova remnants, active galactic nuclei (AGNs), gamma-ray bursts (GRBs), and Galactic microquasar systems usually have power-law emission spectra. Fermi acceleration is the mechanism usually assumed for the acceleration of particles in astrophysical environments. Recent PIC simulations using injected relativistic electron-ion (electro-positron) jets show that acceleration occurs within the downstream jet, rather than by the scattering of particles back and forth across the shock as in Fermi acceleration. Shock acceleration is a ubiquitous phenomenon in astrophysical plasmas. Plasma waves and their associated instabilities (e.g., the Buneman instability, other two-streaming instability, and the Weibel 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. We will review recent PIC simulations which show particle acceleration in jets.
We performed numerical simulations of particle acceleration, magnetic field generation, and emission from shocks in order to understand the observed emission from relativistic jets and supernova remnants. The investigation involves the study of collisionless shocks, where the Weibel instability is responsible for particle acceleration as well as magnetic field generation. A 3-D relativistic particle-in-cell (RPIC) code has been used to investigate the shock processes in electron-positron plasmas. The evolution of theWeibe1 instability and its associated magnetic field generation and particle acceleration are studied with two different jet velocities (0 = 2,5 - slow, fast) corresponding to either outflows in supernova remnants or relativistic jets, such as those found in AGNs and microquasars. Slow jets have intrinsically different structures in both the generated magnetic fields and the accelerated particle spectrum. In particular, the jet head has a very weak magnetic field and the ambient electrons are strongly accelerated and dragged by the jet particles. The simulation results exhibit jitter radiation from inhomogeneous magnetic fields, generated by the Weibel instability, which has different spectral properties than standard synchrotron emission in a homogeneous magnetic field.
We examine homogeneous MHD turbulence and turbulent magnetic reconnection as possible mechanisms for accelerating cosmic ray particles. Test particle calculations are performed using fields from MHD simulations, and initially Maxwellian particle distributions are shown to evolve into power-law distributions. Simple estimates for both the maximum energy attainable and the mean energies of the accelerated particles are fairly successful and are consistent with timescales for flares and cosmic rays.
The results of a numerical study of the diffusive acceleration of charged particles at the termination shock of the solar wind are reported. In the model a realistic magnetic field structure is employed which is similar to that observed in the solar wind. In addition to causing spatial variation of the diffusion tensor, a major effect of the magnetic field is to cause guiding-center drifts of the accelerated particles, both in the solar wind and at the shock. It is demonstrated that the inclusion of the drifts has a large effect on the acceleration. It is concluded, furthermore, that acceleration at the termination shock, in conjunction with drifts, can explain several observed features of the anomalous component.
Particle accelerators are extremely complex systems that are expected to operate on high availability. Predicting impending failures only by utilizing data collected from diagnostic equipment already on board can help operators to avoid installing expensive sensors, unscheduled downtime and associated costs. For this purpose we explore the predictive power of Machine Learning algorithms to detect faulty beams prior to the failure. In this study, we propose a Machine Learning approach to model mapping from a pair of sensors located across the accelerator. While the model is trained to represent normal operation, we evaluate the predictive performance on known faulty beam pulses. We also investigate the model performance on unseen data through k-fold cross-validation. Then we recap the analysis with a neural architecture search and hyperparameter optimization study to fine tune our initial model. In conclusion, this paper will also introduce a sustainable framework that can standardize Machine Learning workflow applied to particle accelerators.
The ever increasing demands placed upon machine performance have resulted in the need for more comprehensive particle accelerator modeling. Computer simulations are key to the success of particle accelerators. Many aspects of particle accelerators rely on computer modeling at some point, sometimes requiring complex simulation tools and massively parallel supercomputing. Examples include the modeling of beams at extreme intensities and densities (toward the quantum degeneracy limit), and with ultra-fine control (down to the level of individual particles). In the future, adaptively tuned models might also be relied upon to provide beam measurements beyond the resolution of existing diagnostics. Much time and effort has been put into creating accelerator software tools, some of which are highly successful. However, there are also shortcomings such as the general inability of existing software to be easily modified to meet changing simulation needs. In this paper possible mitigating strategies are discussed for issues faced by the accelerator community as it endeavors to produce better and more comprehensive modeling tools. This includes lack of coordination between code developers, lack of standards to make codes portable and/or reusable, lack of documentation, among others.
The variety of accelerated particle phenomena in solar flares is reviewed, including particle observation in interplanetary space and gamma-ray, neutron, hard X-ray, and radio emissions. It is emphasized that a significant or perhaps even large fraction of the total solar-flare energy is in accelerated particles. It is pointed out that the majority of the particles that produce impulsive flare phenomena remain trapped at the sun, probably due to confinement in the flaring magnetic loops. Gamma-ray observations are used to calculate the current rate of irradiation of the sun by MeV protons accelerated in solar flares. The current number of escaping protons is compared with irradiation rates of meteorites implied by observations of isotopic anomalies, and it is found that, except for the Ne-21 anomaly, enhanced proton irradiations from the ancient sun could not have been responsible for the observed isotopic anomalies.
Transverse beam parameters in particle accelerators are commonly described using the Twiss parameters, which are experimentally accessible yet inherently limited because they neglect correlations between different transverse coordinates. Such correlations frequently arise from uncompensated cathode magnetic fields or misaligned focusing quadrupoles, affecting beam quality and accelerator performance. To address this limitation, we propose and validate a novel diagnostic method for the complete four-dimensional (4D) transverse beam matrix. Our method involves passing the beam through a beamline comprising both conventional and skew quadrupole magnets, followed by downstream measurements of the resulting two-dimensional (2D) beam profiles. These measurements represent distinct 2D projections of the underlying 4D transverse phase–space distribution. By systematically varying quadrupole strengths, multiple independent projections of the beam phase space are obtained. We reconstruct the original 4D beam matrix from these measured projections using an optimization-based least-square fit, providing fast and robust reconstruction regardless of the specific beamline configuration. Through extensive numerical simulations and realistic particle-tracking studies, we demonstrate the diagnostic’s accuracy, robustness, and capability to achieve reconstruction uncertainties smaller than measurement errors, particularly when employing sufficient numbers of quadrupole scans. This method presents a powerful and flexible approach for comprehensive beam characterization and accelerator tuning.
A Monte Carlo code was developed for the simulation of particle acceleration at astrophysical shocks. The code is implemented in Turbo Pascal on a PC. It is modularized and structured in such a way that modification and maintenance are relatively painless. Monte Carlo simulations of particle acceleration at shocks follow the trajectories of individual particles as they scatter repeatedly across the shock front, gaining energy with each crossing. The particles are assumed to scatter from magnetohydrodynamic (MHD) turbulence on both sides of the shock. A scattering law is used which is related to the assumed form of the turbulence, and the particle and shock parameters. High energy cosmic ray spectra derived from Monte Carlo simulations have observed power law behavior just as the spectra derived from analytic calculations based on a diffusion equation. This high energy behavior is not sensitive to the scattering law used. In contrast with Monte Carlo calculations diffusive calculations rely on the initial injection of supra-thermal particles into the shock environment. Monte Carlo simulations are the only known way to describe the extraction of particles directly from the thermal pool. This was the triumph of the Monte Carlo approach. The question of acceleration efficiency is an important one in the shock acceleration game. The efficiency of shock waves efficient to account for the observed flux of high energy galactic cosmic rays was examined. The efficiency of the acceleration process depends on the thermal particle pick-up and hence the low energy scattering in detail. One of the goals is the self-consistent derivation of the accelerated particle spectra and the MHD turbulence spectra. Presumably the upstream turbulence, which scatters the particles so they can be accelerated, is excited by the streaming accelerated particles and the needed downstream turbulence is convected from the upstream region. The present code is to be modified to include a better description of particle scattering (pitch-angle instead of hard-sphere) and as iterative procedure for treating the self-excitation of the MHD turbulence.
Professor Tor Raubenheimer from the SLAC National Accelerator Laboratory outlines how particle accelerators have evolved and looks at what the future might hold.
The theory of particle acceleration via diffusive shock acceleration (DSA) has been studied in depth by Gosling et al. (1981), van Nes et al. (1984), Mason (2000), Desai et al. (2003), Zank et al. (2006), among many others. Recently, Parker and Zank (2012, 2014) and Parker et al. (2014) using the Advanced Composition Explorer (ACE) shock database at 1 AU explored two questions: does the upstream distribution alone have enough particles to account for the accelerated downstream distribution and can the slope of the downstream accelerated spectrum be explained using DSA? As was shown in this research, diffusive shock acceleration can account for a large population of the shocks. However, Parker and Zank (2012, 2014) and Parker et al. (2014) used a subset of the larger ACE database. Recently, work has successfully been completed that allows for the entire ACE database to be considered in a larger statistical analysis. We explain DSA as it applies to single and multiple shocks and the shock criteria used in this statistical analysis. We calculate the expected injection energy via diffusive shock acceleration given upstream parameters defined from the ACE Solar Wind Electron, Proton, and Alpha Monitor (SWEPAM) data to construct the theoretical upstream distribution. We show the comparison of shock strength derived from diffusive shock acceleration theory to observations in the 50 keV to 5 MeV range from an instrument on ACE. Parameters such as shock velocity, shock obliquity, particle number, and time between shocks are considered. This study is further divided into single and multiple shock categories, with an additional emphasis on forward-forward multiple shock pairs. Finally with regard to forwardforward shock pairs, results comparing injection energies of the first shock, second shock, and second shock with previous energetic population will be given.
The theory of particle acceleration via diffusive shock acceleration (DSA) has been studied in depth by Gosling et al. (1981), van Nes et al. (1984), Mason (2000), Desai et al. (2003), Zank et al. (2006), among many others. Recently, Parker and Zank (2012, 2014) and Parker et al. (2014) using the Advanced Composition Explorer (ACE) shock database at 1 AU explored two questions: does the upstream distribution alone have enough particles to account for the accelerated downstream distribution and can the slope of the downstream accelerated spectrum be explained using DSA? As was shown in this research, diffusive shock acceleration can account for a large population of the shocks. However, Parker and Zank (2012, 2014) and Parker et al. (2014) used a subset of the larger ACE database. Recently, work has successfully been completed that allows for the entire ACE database to be considered in a larger statistical analysis. We explain DSA as it applies to single and multiple shocks and the shock criteria used in this statistical analysis. We calculate the expected injection energy via diffusive shock acceleration given upstream parameters defined from the ACE Solar Wind Electron, Proton, and Alpha Monitor (SWEPAM) data to construct the theoretical upstream distribution. We show the comparison of shock strength derived from diffusive shock acceleration theory to observations in the 50 keV to 5 MeV range from an instrument on ACE. Parameters such as shock velocity, shock obliquity, particle number, and time between shocks are considered. This study is further divided into single and multiple shock categories, with an additional emphasis on forward-forward multiple shock pairs. Finally with regard to forward-forward shock pairs, results comparing injection energies of the first shock, second shock, and second shock with previous energetic population will be given.
Determining the relative contribution of solar flares versus coronal mass ejections in large solar energetic particle (SEP) events is a long-standing problem. Flare-accelerated particles may travel through complex magnetic fields in the eruption region and escape into interplanetary space, thereby contributing to large SEP events. The process by which flare accelerated particles are released into the heliosphere is poorly understood and yet is critical to advancing our understanding of SEPs. In this work, we address the release problem by solving the focused transport equation in the context of a 2.5D ARMS magnetohydrodynamic simulation of a breakout coronal mass ejection (CME)/flare event. We find that particles accelerated by flare reconnection can be released into interplanetary space through interchange reconnection between closed and open field lines. These particles can contribute directly to SEP events and may become an important seed population for further acceleration by CME-driven shocks. Additionally, we find that the energetic particle fluxes in the inner heliosphere remain elevated for an extended period, allowing them to contribute to SEP acceleration by subsequent CMEs. This study represents the first direct particle modeling of how flare-accelerated particles can contribute to major SEP events.
With the advent of increased computational resources and improved algorithms, machine learning-based models are being increasingly applied to complex problems in particle accelerators. However, such data-driven models may provide overly confident predictions with unknown errors and uncertainties. For reliable deployment of machine learning models in high-regret and safety-critical systems such as particle accelerators, estimates of prediction uncertainty are needed along with accurate point predictions. In this investigation, we evaluate Bayesian neural networks (BNN) as an approach that can provide accurate predictions along with reliably quantified uncertainties for particle accelerator problems, and compare their performance with bootstrapped ensembles of neural networks. We select three accelerator setups for this evaluation: a storage ring, a photoinjector, and a linac. The problems span different data volumes and dimensionalities (e.g., scalar predictions as well as image outputs). It is found that BNN provide accurate predictions of the mean along with reliable estimates of predictive uncertainty across the test cases. In this vein, BNN may offer an attractive alternative to deterministic deep learning tools to generate accurate predictions with quantified uncertainties in particle accelerator applications.