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DOE OSTI2023

High Power Solid Target for Positron Source at CEBAF

The progress in the development of a polarized positron injector for the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is presented. The polarized positron beam is generated by a high current polarized electron beam (>1 mA @ 120 MeV) via bremsstrahlung radiation and e+e- pair production in the tungsten target. The simulations show that using an optimized target and positron beamline, the positron injector can provide a cw positron beam with a current larger than 50 nA and a polarization as large as 60%. Injected into the North Linac of CEBAF at an energy of 123 MeV, the positron beam can reach a maximum energy of 12 GeV to perform a rich experimental program. The results of the thermal and structural FEA analysis of the heat load in the target are presented, as well as the simulation results of radiation damage in the target. The performed and planned target material fatigue and radiation damage tests are discussed. This work is supported by the U.S. DOE, Office of Science, Office of Nuclear Physics, Contract No. DE-AC05-06OR23177 and the European Union?s Horizon 2020 research and innovation program under grant agreement No. 824093.

Ushakov, Andriy↗
DOE OSTI2024

Simulations of Positron Capture at Ce+BAF

We present a capture concept for the continuous wave (CW) polarized positron injector for the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab (Ce+BAF). This two-step concept is based on (1) the generation of bremsstrahlung radiation by a longitudinally polarized electron beam 1 mA, 120 MeV, 90% polarization), passing through a tungsten target, and (2) the production of e+e--pairs by these bremsstrahlung photons in the same target. To provide highly-polarized positron beams (>60% polarization) or high-current positron beams (>1 µA) with low polarization for nuclear physics experiments, the positron source requires a flexible capture system with an adjustable energy selection band. The results of beam dynamics simulations and calculations of the power deposited in the positron capture section are presented.

Ushakov, Andriy↗
DOE OSTI2024

SRF Cavity Instability Detection with Machine Learning at CEBAF

During the operation of the Continuous Electron Beam Accelerator Facility (CEBAF), one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities during these trip events. These challenges make the identification of an unstable cavity out of the hundreds installed at CEBAF a difficult and time-consuming task. To tackle these issues, a fast data acquisition system (DAQ) for the legacy SRF cavities has been developed, which records the sample at 5 kHz. A Principal Component Analysis (PCA) approach is being developed to identify anomalous SRF cavity behavior. We will discuss the present status of the DAQ system and PCA model, along with initial performance metrics. Overall, our method offers a practical solution for identifying unstable SRF cavities, contributing to increased beam availability and machine reliability.

Carpenter, A.↗
DOE OSTI2024

TRANSFER LEARNING FOR FIELD EMISSION MITIGATION IN CEBAF SRF CAVITIES

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab operates hundreds of super-conducting radio frequency (SRF) cavities in its two linear accelerators (linacs). Field emission (FE) is an ongoing operational challenge in higher gradient SRF cavities. FE generates high levels of neutron and gamma radiation leading to damaged accelerator hardware and a radiation hazard environment. During machine development periods, we performed gradient scans to record data capturing the relationship between cavity gradients and radiation levels measured throughout the linacs. However, the field emission environment at CEBAF varies considerably over time as the configuration of the radio frequency (RF) gradients changes and due to the changing behaviour of field emitters. An artificial intelligence/machine learning (AI/ML) approach with transfer learning could be a valuable tool to mitigate FE and lower the radiation levels. In this work, we mainly focus on leveraging the RF trip data gathered during CEBAF operations. We develop a transfer learning-based surrogate model for radiation detector readings given RF cavity gradients to track the CEBAF?s changing configuration and environment. Then, we could use the developed model as an optimization process for redistributing the RF gradients within a linac to minimize radiation levels.

Ahammed, K.↗
DOE OSTI2025

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

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

Rajput, Kishansingh [Thomas Jefferson National Acc↗
DOE OSTI2025

Examples of Mission-driven Data Science from Jefferson Lab and ACES

This presentation details mission-driven data science initiatives at Jefferson Lab and the Joint Institute for Advanced Computing on Environmental Studies (ACES). JLab, a U.S. Department of Energy Office of Science national laboratory, operates the Continuous Electron Beam Accelerator Facility (CEBAF), and is the lead institute for the new High Performance Data Facility (HPDF) Hub. The Joint Institute for ACES brings together interdisciplinary teams in health informatics, climate modeling, computer science, and physics to address environmental challenges, including flood modeling. The Hampton Roads region, particularly Norfolk and Virginia Beach, faces increasing flood risks, motivating the need for rapid, reliable, and risk-aware decision support. ACES’s flooding work has a focus on uncertainty quantification (UQ) and machine learning (ML) for coastal flood management. The work is motivated by the increasing vulnerability of communities such as Norfolk and Virginia Beach, Virginia, to frequent coastal flooding events, and the need for rapid, reliable decision support. The research develops computationally efficient ML surrogate models to forecast water levels and flooding risk. A central theme is the quantification and calibration of predictive uncertainty, especially for out-of-distribution (OOD) scenarios, using techniques such as Monte Carlo Dropout, Deep Ensembles, Gaussian Processes, and Deep Quantile Regression (DQR). The study demonstrates that distance-aware UQ is critical for reliable scientific AI, particularly in high-dimensional, safety-critical, and real-time applications.

McSpadden, Diana [Thomas Jefferson National Accele↗
DOE OSTI2026

AI-Ready Semantic Infrastructure for CEBAF: From CED to PALS Knowledge Graphs

JLab and PNNL are jointly developing an AI-ready data ecosystem that exposes the Continuous Electron Beam Acceleration Facility’s (CEBAF’s) operational configuration, lattice description, and control-system channels to agentic optimization frameworks through a standards-based semantic layer. The effort integrates the existing facility-specific CEBAF Element Database (CED) with extensions of the emerging facility-agnostic Particle Accelerator Lattice Standard (PALS) to produce a knowledge graph (KG) containing coherent, machine-interpretable views of devices, signals, and regions. With this KG, CEBAF’s setpoints, readbacks, and device hierarchies become queryable using a uniform declarative graph query language (e.g., Neo4j Cypher), providing intents and inspectable semantics suitable for agentic control. The resulting graph-backed interfaces will allow autonomous agents to retrieve authoritative machine configurations, reason over device- and signal-level relationships, and execute tuning and diagnostic workflows without bespoke CEBAF-specific logic, thereby delivering a scalable pathway from operational data to trustworthy agentic accelerator tuning frameworks.

Zhang, He [Thomas Jefferson National Accelerator F↗
DOE OSTI2026

PARTICULATE STUDY OF NEG PUMPS IRRADIATED IN THE CEBAF TUNNEL

Non-evaporable getter (NEG) pumps are being used to maintain ultra-high vacuum in the beamline of superconducting radio-frequency (SRF) accelerators, such as the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. Because of the sensitivity of the performance of SRF cavities to particulate contamination, it is important to evaluate the integrity of the NEG material after exposure to high radiation during beam operation. The particulate from two NEG pumps based on ZAO® getter alloy was measured with a particle counter in a clean-room. The pumps were assembled onto a hermetically sealed setup which was placed in the CEBAF tunnel. The setup was exposed to 24 h beam operation for ~ 230 days. The total gamma-rays dose measured on the test setup was ~ 1.6 Mrad. The total neutron dose measured at ~ 75 cm of the test setup was ~ 11 krad. The particulate count from the two pumps was measured again in the clean-room after irradiation. Whereas an increase of particulate counts was measured, compared to before irradiation, subsequent measurements indicate the absence of systematically loose particulate. The pumping speed of one of the irradiated pumps was also measured to be consistent with that of a non-irradiated one, corroborating the absence of significant damage to the ZAO® NEG material due to irradiation.

Ciovati, Gianluigi [Thomas Jefferson National Acce↗
DOE OSTI2025

SRF cavity instability detection with machine learning at CEBAF

During the operation of the Continuous Electron Beam Accelerator Facility (CEBAF), one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detectfast transient instabilities during these trip events. These challenges make the identification of an unstable cavity out of the hundreds installed at CEBAF a difficult and time-consuming task. To tackle these issues, a fast data acquisition system (DAQ) for the legacy SRF cavities has been developed, which records the sample at 5 kHz. An unsupervised learning framework has been developed to identify anomalous SRF cavity behavior. We will discuss the present status of the DAQ system and our framework, along with recent successes in detecting anomalous cavity behavior. Overall, our method offers a practical solution for identifying unstable SRF cavities, contributing to increased beam availability and machine reliability.

Accelerator Physics↗
DOE OSTI2025

Polarized positrons at Ce\(^+\)BAF

A baseline concept for a continuous wave (CW) polarized positron injector was developed for the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab [1]. This concept is based on positron beam generation by a high current polarized electron beam (1 mA, 120 MeV, >85% polarization) irradiating a 4 mm thick rotating water-cooled tungsten target or liquid metal target. The update on the development of the Ce+BAF injector concept including the polarized electron source, the development of high power targets, simulations of positron capture, the design of the transport line from the positron injector to the CEBAF North Linac and the planned experiment to measure the upper limits of transverse and longitudinal emittances accepted by the 12 GeV CEBAF optics are presented. [1] J. Grames et al., "Positron beams at Ce+BAF", in Proc. 14th Int. Particle Accelerator Conf. (IPAC’23), Venice, Italy, MOPL152, pp. 896–899 (2023). doi:10.18429/JACoW-IPAC2023-MOPL152

Ushakov, Andriy↗
DOE OSTI2025

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]↗
DOE OSTI2024

Longitudinal Solid P Longitudinal Solid Polarized Target for CL get for CLAS12 and Study of Spin AS12 and Study of Spin Structure of Nucleons

A suite of experiments measuring target-spin observables in electron-nucleon scattering (dubbed Run Group C) was conducted at Jefferson Lab's Hall B in Newport News, VA with a new polarized nuclear target known as 'APOLLO' (Ammonia POLarized LOngitudinally). This innovative target is engineered to seamlessly integrate with the advanced 12GeV CEBAF (Continuous Electron Beam Accelerator Facility) accelerator and the Hall B CLAS12 (12 GeV CEBAF Large Acceptance Spectrometer) detector array. The 'APOLLO' target harnesses the power of Dynamic Nuclear Polarization (DNP) to achieve longitudinal polarization of solid ammonia, thereby creating a net polarization in both protons (NH3) and deuterons (ND3). These samples are subjected to a 5 Tesla magnetic field produced by the CLAS12 spectrometer central solenoid, cooled to 1 K using helium evaporation, and subsequently exposed to microwave radiation at a frequency of 140 GHz. This project was made possible through a collaborative partnership between the Jefferson Lab Target Group, Old Dominion University, University of Virginia, Christopher Newport University, and the CLAS Collaboration. Integrating the APOLLO target into CLAS12 presented unique challenges because of its specific spatial and dimensional constraints. This dissertation will highlight the innovative solutions developed to address these challenges, including the creation of a new target material transport system, the incorporation of superconducting magnetic correction coils, and the development of a customized Nuclear Magnetic Resonance (NMR) system. In addition to a detailed description of the development and operation of APOLLO, and an overview of RGC, this document also describes the global data fitting of proton asymmetries in the Deep Inelastic scattering (DIS) and the Resonance regions, and it offers insights into the comprehensive analysis of target polarization data.

Pandey, Pushpa↗
DOE OSTI2021

Electrostatic Design and Characterization of a 200 keV Photogun and Wien Spin Rotator

High-energy nuclear physics experiments at the Jefferson Lab Continuous Electron Beam Accelerator Facility (CEBAF) require high spin-polarization electron beams produced from strained super-lattice GaAs photocathodes activated to negative electron affinity in a high voltage photogun operating at 130 kV dc. A pair of Wien filter spin rotators in the injector provides precise control of the electron beam polarization at the end station target. An upgrade of the CEBAF injector to better support the upcoming Moller experiment requires increasing the electron beam energy to 200 keV, resulting in better transmission through injector apertures and improved photocathode lifetime. In addition, the energy increase is expected to reduce unwanted helicity correlated intensity and position systematics. These requirements led to the design of a shielding electrode described in this work, which minimizes the electric field at the triple-point junction and linearizes the potential along the insulator, thus reducing the risk of field emission induced insulator arcing. The Wien spin rotator design was modified for increasing the electric field from 1.6 to 2.7 MV/m and the magnetic field from 9.1 to 13 mT. The upgrades required detailed modeling in Solidworks, electrostatic simulations using CST, beam dynamics using GPT, device implementation, and in situ high voltage characterization of the world?s first 200 keV polarized photoelectron gun and compatible Wien filter spin rotator.

Palacios-Serrano, Gabriel↗
DOE OSTI2022

ION PRODUCTION AND MITIGATION IN DC HIGH-VOLTAGE PHOTO-GUNS

One of the biggest obstacles to operating a GaAs polarized electron source with a long charge lifetime is the mitigation of ion back-bombardment. Several techniques exist to either clear ions from the accelerator or to mitigate ion damage of the photocathode. Predicting the effectiveness of these techniques requires sophisticated simulation models of electron impact ionization within the photo-gun. In this work, the effectiveness of applying a positive anode bias voltage to mitigate ion damage and increase the charge lifetime of the GaAs photocath?ode was studied over three run periods at the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab (JLab). The charge lifetime with the biased anode configuration was 1.80±0.51 times longer than the lifetime of the usual grounded anode configuration. Sim?ulations of ionization within the CEBAF photo-gun and adjacent beamline were made using General Particle Tracer (GPT) and a new C++ custom element to predict and explain the substantial improvement in charge lifetime. The experimental results and the development of the ionization custom element, with its use in simulations of ion back-bombardment with the biased anode, are described in detail.

Yoskowitz, Joshua↗
DOE OSTI2020

Deeply Virtual Compton Scattering at Hall A, Jefferson Lab

The Standard Model of particle physics de?nes quarks and leptons as the basic building blocks of all matter. The interaction between them are mediated by force carrying gauge bosons. Quantum Chromo Dynamics (QCD), the theory that explains the strong interaction is still not complete enough to derive the physical observables of a Quark-Gluon system from the fundamental degrees of freedom of its constituents. Experimentally observable single particle densities provide important insights into our understanding of the quark-gluon system and hence help fill in the gaps of QCD. Generalized Parton Distributions (GPDs) provide simultaneous information of both spacial and longitudinal momentum distributions of constituents of a quark-gluon system. Deeply Virtual Compton Scattering (DVCS) is understood to be the simplest and cleanest process to access GPDs. Even though the exclusive DVCS is simple to understand, the experimental process however, is complex with the Bethe-Heitler and e + p ? e + ? + N? being in the mix of the electron proton scattering e+p ? e+?+X. Over the years, 3 generations of DVCS experiments have been conducted in the Experimental Hall-A of Thomas Jefferson National Accelerator Facility (JLab). This thesis presents the extraction of DVCS cross section in 9 total kinematic points from the 3rd generation experiment (DVCS3) conducted after the 12 GeV upgrade of the Continuous Electron Beam Accelerator Facility (CEBAF) of JLab during Fall 2014 - Fall 2016.

Hashir Rashad, Mohamed Nuhman↗
DOE OSTI2023

Design and Contruction of a Longitudinally Polarized Solid Nuclear Target for CLAS12

A new polarized nuclear target has been developed, constructed, and deployed at Jefferson Laboratory in Newport News, VA for use with the upgraded 12 GeV CEBAF (Continuous Electron Beam Accelerator Facility) accelerator and the Hall B CLAS12 (12 GeV CEBAF Large Acceptance Spectrometer) detector array. This ?APOLLO? (Ammonia PO-Larized LOngitudinally) target is a longitudinally polarized, solid ammonia, nuclear target which employs DNP (Dynamic Nuclear Polarization) to induce a net polarization in samples of protons (NH3) and deuterons (ND3) cooled to 1 K via helium evaporation, held in a 5 T polarizing field supplied by the CLAS12 spectrometer, and irradiated with 140 GHz microwave radiation. It was utilized in the RGC (Run Group C) experiment suite through a collaboration of the JLab Target Group, Old Dominion University, Christopher Newport University, the University of Virginia, and the CLAS Collaboration. RGC comprised six experiments which measured multiple spin-dependent observables across a wide kinematic phase space for use in nucleon spin studies. The dimensional constraints necessary for the incorporation of APOLLO into CLAS12, as well as the considerations necessary to utilize the CLAS12 solenoid, introduced unique challenges to the target design. This document presents the innovative solutions developed for these challenges including a novel material transport system, superconducting magnetic correction coils, and an all new bespoke NMR (Nuclear Magnetic Resonance) system. In addition to a detailed description of the complete target system and an initial report of the RGC experimental run, it will also present a study of Quark-Hadron Duality in the g1 spin structure function based on Hall B EG1b data and pQCD fits from the JAM (Jefferson Lab Angular Momentum) Collaboration.

Lagerquist, Victoria Lagerquist↗
DOE OSTI2024

Error and Correction Analysis for the FFA@CEBAF Energy Upgrade

An energy upgrade design for the Continuous Electron Beam Accelerator Facility (CEBAF) is under development, using fixed field alternating gradient (FFA) return arcs to recirculate electron beam up to an additional five times through the accelerating structures at CEBAF. A necessary component of any large accelerator is a beam steering and optical correction system. Small environmental changes and system errors can lower beam quality or even shut down the machine; and in pursuit of the scientific mission of JLab, high quality electron beams must be delivered to the experimental halls on a predictable schedule. Correction in the novel FFA arcs of the current upgrade design is complicated by several factors. These complexities inform the choice of correction algorithm structure and parameter values. A baseline algorithm in addition to diagnostic and correction hardware configuration is presented. The effect of this correction protocol is shown with respect to estimated errors, and several possible extensions of the algorithm are discussed. This work presents an important proof of concept for the FFA@CEBAF design effort, and provides a functional correction strategy which may be simply adjusted and optimized for future design changes.

Coxe, Alex [Old Dominion Univ., Norfolk, VA (Unite↗
DOE OSTI2026

A STUDY OF SHORT-RANGE CORRELATED PAIR FORMATION MECHANISMS

Short-Range Correlations (SRCs) refers to pairs of nucleons that are temporary high density fluctuations with high relative momenta and lower center-of-mass momenta com pared to the nuclear Fermi momentum (kF). SRCs account for 20–25% of the nucleons in medium to heavy nuclei, make up essentially all nucleons with momentum greater than kF, and contribute most of the kinetic energy carried by nucleons in nuclei. The existing semi-inclusive and exclusive measurements only cover a handful of light nu clei or heavy elements. This does not allow for a systematic study of the dependence of SRC pairs on nuclear mass and proton-neutron asymmetry. It also does not allow for insights into SRC pairing mechanisms. Therefore, we systematically studied the individual probabilities for finding SRC protons in symmetric and neutron-rich asymmetric nuclei d, 9Be, 10B, 11B, 12C, 40Ca, 48Ca, 54Fe, and 197Au. We measured the (e,e'p) reaction in kinematics dominated by scattering off mean-field nucleons (k = kF) and nucleons in SRC pairs (k = kF) at the Thomas Jefferson National Accelerator Facility (JLab) in Hall C of the Continuous Electron Beam Accelerator Facility (CEBAF) in the Fall of 2022. The measured results were used to determine the SRC pairing probabilities for protons to examine how pairing depends on nuclear mass, proton-neutron asymmetry, and nuclear shell structure. The extracted cross-section ratios were also compared to theoretical calculations. We found that SRC pair formation depends more on the nuclear shell structure with sharp increases locally within the general trend of a slower increase with larger A. We also found that intra-shell pairing has a much larger influence than inter-shell pairing. Comparisons to theory suggest that angular momentum selection rules are important to SRC pair formation and can provide new constraints for new theoretical models.

Swan, Noah [Old Dominion Univ., Norfolk, VA (Unite↗