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At least 109 records · Page 6

The role of electric dominance for particle injection in relativistic reconnection

ABSTRACT Magnetic reconnection in relativistic plasmas – where the magnetization $\sigma \gg 1$ – is regarded as an efficient particle accelerator, capable of explaining the most dramatic astrophysical flares. We employ two-dimensional (2D) particle-in-cell simulations of relativistic pair-plasma reconnection with vanishing guide field and outflow boundaries to quantify the impact of the energy gain occurring in regions of electric dominance ($E\gt B$) for the early stages of particle acceleration (i.e. the ‘injection’ stage). Given an injection threshold energy $\epsilon ^\ast =\sigma /4$ for the particles that eventually attain energy $\epsilon _{\rm T}$ by time T, we calculate the mean fractional contribution $\zeta (\epsilon ^\ast ,\epsilon _{\rm T})$ by $E\gt B$ fields to particle energization at the time when the threshold $\epsilon ^\ast$ is reached. We find that $\zeta$ monotonically increases with $\sigma$ and $\epsilon _{\rm T}$; for $\sigma \gtrsim 50$ and $\epsilon _{\rm T}/\sigma \gtrsim 8$, we find that $\gtrsim 80~{{\ \rm per\ cent}}$ of the energy gain obtained before reaching $\epsilon ^\ast =\sigma /4$ occurs in $E\gt B$ regions. We find that $\zeta$ is independent of simulation box size $L_x$, as long as $\epsilon _{\rm T}$ is normalized to the maximum particle energy, which scales as $\epsilon _{\rm max}\propto L_{\rm x}^{1/2}$ in 2D. The distribution of energy gains $\epsilon _{\chi }$ acquired in $E\gt B$ regions can be modelled as $\mathrm{ d}N/\mathrm{ d}\epsilon _{\chi }\propto \epsilon _{\chi }^{-0.35}\exp [-(\epsilon _{\chi }/0.06\, \sigma)^{0.5}]$. Our results help assess the role of electric dominance in relativistic reconnection with vanishing guide fields, which is realized in the magnetospheres of black holes and neutron stars.

Gupta, Sanya (ORCID:0000000151944384)↗

Towards Unlocking Insights from Logbooks Using AI

Electronic logbooks contain valuable information about activities and events concerning their associated particle accelerator facilities. However, the highly technical nature of logbook entries can hinder their usability and automation. As natural language processing (NLP) continues advancing, it offers opportunities to address various challenges that logbooks present. This work explores jointly testing a tailored Retrieval Augmented Generation (RAG) model for enhancing the usability of particle accelerator logbooks at institutes like DESY, BESSY, Fermilab, BNL, SLAC, LBNL, and CERN. The RAG model uses a corpus built on logbook contributions and aims to unlock insights from these logbooks by leveraging retrieval over facility datasets, including discussion about potential multimodal sources. Our goals are to increase the FAIR-ness (findability, accessibility, interoperability, and reusability) of logbooks by exploiting their information content to streamline everyday use, enable macro-analysis for root cause analysis, and facilitate problem-solving automation.

43 PARTICLE ACCELERATORS↗

Flat Spectra of Energetic Particles in Interplanetary Shock Precursors

The observed energy spectra of accelerated particles at interplanetary shocks often do not match the diffusive shock acceleration (DSA) theory predictions. In some cases, the particle flux forms a plateau over a wide range of energies, extending upstream of the shock for up to seven flux e-folds before submerging into the background spectrum. Remarkably, at and downstream of the shock we have studied in detail, the flux falls off in energy as ϵ -1 , consistent with the DSA prediction for a strong shock. The upstream plateau suggests a particle transport mechanism different from those traditionally employed in DSA models. We show that a standard (linear) DSA solution based on a widely accepted diffusive particle transport with an underlying resonant wave–particle interaction is inconsistent with the plateau in the particle flux. To resolve this contradiction, we modify the DSA theory in two ways. First, we include a dependence of the particle diffusivity κ on the particle flux F (nonlinear particle transport). Second, we invoke short-scale magnetic perturbations that are self-consistently generated by, but not resonant with, accelerated particles. They lead to the particle diffusivity increasing with the particle energy as ∝ϵ 3/2 that simultaneously decreases with the particle flux as 1/F. The combination of these two trends results in the flat spectrum upstream. We speculate that nonmonotonic spatial variations of the upstream spectrum, apart from being time-dependent, may also result from non-DSA acceleration mechanisms at work upstream, such as stochastic Fermi or magnetic pumping acceleration.

79 ASTRONOMY AND ASTROPHYSICS↗

Global heliospheric termination shock strength in the solar–interstellar interaction

A heliospheric termination shock (HTS) surrounds our Solar System at approximately 100 astronomical units from the Sun, where the expanding solar wind (SW) is compressed and heated before encountering the interstellar medium. HTS-accelerated particles govern the pressure balance with the interstellar medium, but little is known about the global properties of the HTS beyond in situ measurements from Voyager in only two directions of the sky. Here we fill this gap by extracting the HTS strength using particle-in-cell, test particle and magnetohydrodynamic simulations, constrained by Interstellar Boundary Explorer observations of energetic neutral atoms produced from HTS-accelerated particles. Our results reveal there is a higher compression near the poles during solar minimum compared with solar maximum due to the higher Mach number flow. North–south asymmetries arise from the disparate evolution of the polar coronal holes, while minimum compression near the flanks is probably due to SW slowing from mass loading over a greater distance to the HTS. The results imply a strong connection between the HTS strength and the SW and interstellar medium dynamics.

Astrophysical plasmas↗

Pseudospectral particle-in-cell formulation with arbitrary charge and current-density time dependencies for the modeling of relativistic plasmas

This paper introduces a formulation of the particle-in-cell (PIC) method for the modeling of relativistic plasmas, that leverages the ability of the pseudospectral analytical time-domain solver (PSATD) to handle arbitrary time dependencies of the charge and current densities during one PIC cycle (applied to second-order polynomial dependencies here). Here, the formulation is applied to a modified set of Maxwell's equations that was proposed earlier in the context of divergence cleaning, and to recently proposed extensions of the PSATD-PIC algorithm. Detailed analysis and testings revealed that, under some condition, the formulation can expand the range of numerical parameters under which PIC simulations are stable and accurate when modeling relativistic plasmas such as, e.g., plasma-based particle accelerators.

43 PARTICLE ACCELERATORS↗

Preliminary study of auto-differentiation algorithm in beam dynamics with stochastic process

Modern particle accelerator optimization requires sophisticated computational methods to address the inherently stochastic nature of beam dynamics. This research develops a framework applying AD to SDEs that specifically addresses beam dynamics challenges in particle accelerators, focusing on accurately modeling and optimizing beam behavior in regimes dominated by stochastic processes. By incorporating key physical phenomena such as synchrotron radiation, wakefield effects, and quantum excitation, the framework aims to provide auto differentiation on the figure of merit of the phase space evolution and beam dynamics. The methodology will enable effective optimization method in a dynamic system with stochastic process.

Accelerator Physics↗

A plan to revitalize the domestic superconducting radio-frequency industry

Superconducting radio-frequency (SRF) cavities are essential building blocks of modern particle accelerators for scientific research, and they offer unique capabilities that could be transformative for commercial applications. Growth of the domestic SRF industry in North America has faced several challenges over the past decades, as most of the international demand for cavities was supplied by European vendors. This contribution provides a brief review of the domestic industrial vendor space, an outlook of the global demand for SRF cavities and an outline of the challenges leading to this supply chain deficiency. One of the main challenges towards establishing a robust domestic SRF industry has been the large uncertainty in the demand. Meanwhile, research and development activities to raise technical readiness of SRF accelerators for industrial use have continued and several potential markets are emerging that may offer a consistent and growing demand for SRF cavities. Finally, reasons and means of establishing and sustaining competitive domestic suppliers are described.

Accelerator Physics↗

Accelerating particle-in-cell kinetic plasma simulations via reduced-order modeling of space-charge dynamics using dynamic mode decomposition

We present a data-driven reduced-order modeling of the space-charge dynamics for electromagnetic particle-in-cell (EMPIC) plasma simulations based on dynamic mode decomposition (DMD). The dynamics of the charged particles in kinetic plasma simulations such as EMPIC is manifested through the plasma current density defined along the edges of the spatial mesh. We showcase the efficacy of DMD in modeling the time evolution of current density through a low-dimensional feature space. Not only do such DMD based predictive reduced-order models help accelerate EMPIC simulations, they also have the potential to facilitate investigative analysis and control applications. Here, we demonstrate the proposed DMD-EMPIC scheme for reduced-order modeling of current density and speedup in EMPIC simulations involving electron beam under the influence of magnetic field, virtual cathode oscillations, and backward wave oscillator.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗

Multiwavelength Afterglow Analysis of GRB 221009A: Unveiling the Evolution of a Cooling Break in a Wind-like Medium

Gamma-ray bursts (GRBs) are the most energetic explosions in the Universe, and their afterglow emission provides an opportunity to probe the physics of relativistic shock waves in an extreme environment. Several key pieces for completing the picture of GRB afterglow physics are still missing, including jet properties, the emission mechanism, and particle acceleration. Here, we present a study of the afterglow emission of GRB 221009A, the most energetic GRB ever observed. Using optical, X-ray, and gamma-ray data up to approximately 2 days after the trigger, we trace the evolution of the multiwavelength spectrum and the physical parameters behind the emission process. The broadband spectrum is consistent with the synchrotron emission emitted by relativistic electrons with its index of p = 2.29 ± 0.02. We identify a break energy at keV and an exponential cutoff at GeV in the observed multiwavelength spectrum. The break energy increases in time from $16.0^{+7.1}_{-4.9}$ keV at 0.65 days to $46.8^{+25.0}_{-15.5}$ keV at 1.68 days, favoring a stellar-wind-like profile of the circumburst medium with k = 2.4 ± 0.1 as in ρ(r) ∝ r -k . The high-energy attenuation at around 0.4 to 4 GeV is attributed to the maximum of the particle acceleration in the relativistic shock wave. This study confirms that the synchrotron process can explain the multiwavelength afterglow emission and its evolution.

79 ASTRONOMY AND ASTROPHYSICS↗

Multiphysics modeling of accelerators through code integration

This work aims to improve the ability of particle accelerator researchers to develop high-performance accelerator cavity designs by creating an overall multiphysics framework that integrates and couples existing application codes. This framework will allow accelerator researchers to build multiphysics models that will optimize cavity design, improve understanding of whole-device performance, and reduce the development and fabrication costs of accelerator research. We utilize the open-source VizSchema data standard as an intermediate data structure interface layer to standardize interfaces between individual application codes. VizScema is extensively documented online, and plugins for VizSchema are available for popular visualization packages, including VisIt and ParaView. Currently, the work focuses on coupling the EM field solver COMSOL and the electron gun code MICHELLE to allow COMSOL field-solve results to be seamlessly used by MICHELLE for particle-solve. Later work will extend this integration to include other fields, particles, and thermodynamics simulation codes.

43 PARTICLE ACCELERATORS↗

MLOps for Beam Controls

Machine learning operations (MLOps) is the standardization and streamlining of the ML development lifecycle to address the challenges associated with large-scale machine learning applications. The full MLOps pipeline consists of open-source tools: DataHub, MinIO and MLflow. It is being used for dataset management and model development to handle changing data dependencies, varying business needs, reproducibility, and diverse teams working with differing tools and skills. To demonstrate the completion of an MLOps pipeline for particle accelerator operations, we are deploying a simple script that computes settings for the Booster’s gradient magnet power supply. Once the demonstration is complete, we will develop and deploy ML-based optimization algorithms to improve Booster’s overall efficiency. This MLOps pipeline opens the gate to systematically develop and deploy ML applications for accelerator controls and diagnostics.

43 PARTICLE ACCELERATORS↗

MLOps for Beam Controls

Machine learning operations (MLOps) is the standardization and streamlining of the ML development lifecycle to address the challenges associated with large-scale machine learning applications. The full MLOps pipeline consists of open-source tools: DataHub, MinIO and MLflow. It is being used for dataset management and model development to handle changing data dependencies, varying business needs, reproducibility, and diverse teams working with differing tools and skills. To demonstrate the completion of an MLOps pipeline for particle accelerator operations, we are deploying a simple script that computes settings for the Booster’s gradient magnet power supply. Once the demonstration is complete, we will develop and deploy ML-based optimization algorithms to improve Booster’s overall efficiency. This MLOps pipeline opens the gate to systematically develop and deploy ML applications for accelerator controls and diagnostics.

43 PARTICLE ACCELERATORS↗

MLOps for Beam Controls

Machine learning operations (MLOps) is the standardization and streamlining of the ML development lifecycle to address the challenges associated with large-scale machine learning applications. The full MLOps pipeline consists of open-source tools: DataHub, MinIO and MLflow. It is being used for dataset management and model development to handle changing data dependencies, varying business needs, reproducibility, and diverse teams working with differing tools and skills. To demonstrate the completion of an MLOps pipeline for particle accelerator operations, we are deploying a simple script that computes settings for the Booster’s gradient magnet power supply. Once the demonstration is complete, we will develop and deploy ML-based optimization algorithms to improve Booster’s overall efficiency. This MLOps pipeline opens the gate to systematically develop and deploy ML applications for accelerator controls and diagnostics.

43 PARTICLE ACCELERATORS↗

Accelerator Neutrinos

Neutrino beams generated by particle accelerators are essential for probing fundamental physics. This presentation will examine the creation of high-intensity, well-collimated neutrino beams, crucial for long-baseline experiments like DUNE and NOvA. These experiments are pushing proton beam power to multi-MW levels and utilizing large-scale detectors to overcome the challenge of limited event statistics. At LBNF, the DUNE experiment will rigorously test the three-neutrino flavor model and explore CP violation by analyzing oscillation signatures in high-intensity νμ(νμˉ)νμ​(νμ​ˉ​) to νe(νeˉ)νe​(νe​ˉ​) beams. We'll explore the technical complexities of beamline components, the drive towards higher beam powers, and the strategies for measuring and managing neutrino flux. The presentation will also cover advancements in neutrino beam instrumentation and efforts to enhance beam precision, which are key to achieving the next generation of multi-megawatt accelerator facilities. By reviewing past achievements and future prospects, this talk aims to provide a clear overview of the current state and future potential of neutrino beam technology.

Ganguly, Sudeshna↗

Radiation damage effects in beryllium for next generation neutrino beam targetry (Final Technical Report)

Current and future high-power accelerators put severe requirements on materials used for target and beam windows and target facilities have been recognized as a critical challenge in development of future particle accelerators. In accelerators, window and target materials are exposed to extreme conditions, which include bombardment with very high energy protons (1- 100 GeV) and thermomechanical shock waves. Radiation can cause direct damage in the material, and it leads to production of transmutation products (especially helium), both phenomena having a potential adverse effect on the stability and durability of the target/window material. At high enough temperatures, He can aggregate to form gas bubbles, which in turn cause significant dimensional changes (swelling), enable easy crack propagation, and eventually cause failure by fracture. On the other hand, if the temperature is too low, radiation damage accumulates in the form of internal defects (e.g., dislocations), leading to hardening and a decreased ductility of the material. In this project, we will focus on beryllium since it is considered to be one of the candidate materials for beam windows and targets in the next-generation proton accelerators, e.g., the Long Baseline Neutrino Facility (LBNF). Radiation effects in Be have been studied in the context of nuclear fusion reactor applications. However, key differences exist between reactor and accelerator conditions, including neutron vs. proton irradiation, continuous vs. pulsed beam flux, much higher energies of bombarding particles in accelerators, and higher operating temperatures for typical reactors. For example, the impact of beam pulsing on the radiation damage and the He bubble kinetics is largely unknown. While results obtained on Be from fusion research might not be directly transferrable to understanding target materials, there is an opportunity to bring state-of-the-art tools from materials research in nuclear reactors to aid design of target and beam window materials in high-power accelerators. To this end, the overarching goal of this project are to develop an experimentally-validated computational framework capable of predicting radiation damage evolution in beryllium relevant to beam window and target conditions, focusing on He bubble formation and growth as a function of irradiation temperature. Our model will be based on the cluster dynamics formalism, where size distribution of defects and He bubbles is simulated as a function of time, temperature, and radiation dose. Parameters for the model will be taken from published experiments and from high-fidelity atomistic simulations proposed in this project. In addition, we will carry out a series of targeted ex-situ and in-situ dual-beam experiments using low-energy protons to provide critical data for validation of the model on the effects of radiation on He clustering, He bubble distribution, and dislocation loop density/size in proton irradiated Be.

36 MATERIALS SCIENCE↗

Effects of Surface Treatments on the Outgassing of 3D-Printed 316L Stainless Steel.

Fermi National Accelerator Laboratory (Fermilab) is a world leader in designing and operating particle accelerators for nuclear and particle physics research. These accelerators rely on electric fields to propel particle beams, which are then directed and focused using magnetic fields. To ensure these beams travel without interference, they must move through an ultra-high vacuum environment, typically around 10⁻⁹ torr. Maintaining such a vacuum requires materials with exceptionally low outgassing rates. 316L stainless steel is a common material choice for vacuum systems due to its low outgassing rates, excellent corrosion resistance, non-magnetic properties, and high mechanical strength. When manufactured using traditional methods like machining, extrusion, and casting, the vacuum properties of 316L stainless steel are well understood. However, the behavior of 316L stainless steel produced through additive manufacturing, such as Direct Metal Laser Sintering (DMLS), is not as well documented. This study aims to determine the outgassing rate of 316L stainless steel fabricated using DMLS. The investigation focuses on measuring the outgassing rates of 1.5 2 vacuum reducers and evaluating the effects of surface preparation techniques, such as mechanical polishing and electropolishing, on the outgassing performance. Understanding the outgassing performance of 3D-printed 316L stainless steel could enable Fermilab engineers to adopt this fabrication method for producing complex parts designed for ultra-high vacuum environments. These advancements have the potential to benefit projects, such as the Long-Baseline Neutrino Facility (LBNF) and the Proton Improvement Plan-II (PIP-II), by improving design flexibility and fostering innovation in accelerator development.

Cloud, Jaiden [Central State University]↗