Carpenter, A. | Engineering Papers

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 OSTI2024

Improved Beam Loss Accounting with Fast Data Acquisition (DAQ) Chassis

Identifying the source of beam loss events in the CE-BAF accelerator can be a challenging task. However, with our new prototype system, this task becomes more effi-cient. The system, developed in the fall of 2022, utilizes a dispersive beam position monitor (BPM) and the exist-ing switched electrode electronics BPM hardware. Previ-ously a commercial off-the-shelf data acquisition (DAQ) system was employed to capture BPM wire signals at a sample rate of 20 kS/s. The fast shutdown signal triggered the system, which disables the beam at the injector. Analysis of beam position and energy variation before a beam loss event was used to determine if the beam loss event was associated with an energy transient. The proto-type system, implemented using National Instruments hardware and LabVIEW® software, relied on a software trigger. Manual post-processing was required to ascertain whether the fault was due to an un-tripped cavity with a gradient or phase transient. This work focuses on deploying a Fast DAQ Chassis to monitor BPM hardware in real time and during beam loss events. This system was originally developed and in-stalled in CEBAF to monitor the time-domain RF control signals in the legacy analog RF systems. This technology was leveraged to also monitor BPM signals. As the new system employs a hardware trigger, developing tools to automatically identify faults linked to energy transients unrelated to cavity faults will be straightforward. This paper will discuss the project's initial updates, underlin-ing the crucial role of each member of our team in this achievement

Tiskumara, J.↗

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 OSTI2024

SRF CAVITY FAULT PREDICTION USING DEEP LEARNING AT JEFFERSON LAB

In this study, we present a deep learning-based pipeline for predicting superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelera-tor Facility (CEBAF) at Jefferson Lab. We leverage pre-fault RF signals from C100-type cavities and employ deep learning to predict faults in advance of their onset. We train a binary classifier model to distinguish between stable and impending fault signals. Test results show accuracies exceeding 99% for distinguishing between normal signals and pre-fault signals from a class of more slowly developing fault types, such as microphonics. We describe results from a proof-of-principle demonstration on a realistic, imbalanced data set and report performance metrics. Encouraging results suggest that future SRF systems could leverage this framework and implement measures to mitigate the onset in more slowly developing fault types.

Rahman, M.↗

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

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

Time-resolved X-ray diffraction diagnostic development for the National Ignition Facility

Here we present the development of an experimental platform that can collect four frames of x-ray diffraction data along a single line of sight during laser-driven, dynamic-compression experiments at the National Ignition Facility. The platform is comprised of a diagnostic imager built around ultrafast sensors with a 2-ns integration time, a custom target assembly that serves also to shield the imager, and a 10-ns duration, quasi-monochromatic x-ray source produced by laser-generated plasma. We demonstrate the performance with diffraction data for Pb ramp compressed to 150 GPa and illuminated by a Ge x-ray source that produces ∼7 × 10 11 , 10.25-keV photons/ns at the 400 μm diameter sample.

36 MATERIALS SCIENCE↗

DOE OSTI2024

3D Integrated Sensing Solutions

We will present the proposed research project to develop 3D-integrated sensors using advanced manufacturing capability for novel sensors, heterogeneously integrated with energy efficient readout circuits. The lack of precision timing in particle tracking detectors and the absence of low power, high throughput communications solutions to read them out limits progress in multiple fields of fundamental science. The aim of the project is to develop technology to enable large-scale particle detectors with 3D-integrated ASIC designs to simultaneously achieve 10 μm position resolution and 10 ps precision timing, with low-power consumption and high throughput rates. This research program leverages the unique combination of facilities and cross-disciplinary expertise of scientists and engineers at SLAC, FNAL, and LLNL and industrial partners.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Uncertainty Aware Deep Learning for Fault Prediction Using Multivariate Time Series Signals

The superconducting radio-frequency cavities are a crucial component of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. When a cavity faults, beam delivery to experimental end users is disrupted. Prediction of cavity faults prior to onset is essential to reduce operation and maintenance costs. In this work, a parallel long short-term memory (LSTM)-convolution neural network (CNN)-based deep learning (DL) model is proposed to predict impending faults using pre-fault signals. Further, we introduce an uncertainty quantification approach using Monte Carlo dropout with the LSTM-CNN model to ascertain confidence in the prediction. The model was tested using multivariate time series signals from stable cavity operations and before faults. Initial results show that on the test dataset, the model can identify impending faults before their onset with an average 10-fold cross validation accuracy of 97.39% and a standard deviation of 0.12% using a 100-ms time window. It is also observed that the model performs better as the prediction time moves closer to the fault onset. For additional context, we compare the performance of the model with three machine-learning-based (ML) fault prediction models. Our proposed parallel LSTM-CNN-based DL method shows better performance than the ML-based methods.

Rahman, Md Monibor↗

DOE OSTI2022

Design of the high-yield time-gated x-ray hot-spot imager for OMEGA

Time-resolved x-ray self-emission imaging of hot spots in inertial confinement fusion experiments along several lines of sight provides critical information on the pressure and the transient morphology of the hot spot on the University of Rochester’s OMEGA Laser System. At least three quasi-orthogonal lines of sight are required to infer the tomographic information of the hot spots of deuterium–tritium cryogenic layered implosions. OMEGA currently has two time-gated x-ray hot-spot imagers: the time-resolved Kirkpatrick–Baez x-ray microscope and the single-line-of-sight, time-resolved x-ray imager (SLOS-TRXI). The time-gated x-ray hot-spot imager (XRHSI) is being developed for use on OMEGA as the third line of sight for the high-yield operation of up to 4 × 10 14 neutrons. XRHSI follows the SLOS-TRXI concept; however, it will have improved spatial and temporal resolutions of 5 μm and 20 ps, respectively. The simultaneous operation of the three instruments will provide 3-D reconstructions of the assembled hot-spot fuel at various times through peak thermonuclear output. The technical approach consists of a pinhole array imager and demagnifying time-dilation drift tube that are coupled to two side-by-side hybrid complementary metal–oxide semiconductor (hCMOS) image sensors. To minimize the background and to harden the diagnostics, an angled drift-tube assembly shifting the hCMOS sensors out of the direct line of sight and neutron shielding will be applied. As a result, the technical design space for the instrument will be discussed and the conceptual design will be presented.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

A 2–4 keV multilayer mirrored channel for the NIF Dante system

During inertial confinement fusion experiments at the National Ignition Facility (NIF), a capsule filled with deuterium and tritium (DT) gas, surrounded by a DT ice layer and a high-density carbon ablator, is driven to the temperature and densities required to initiate fusion. In the indirect method, 2 MJ of NIF laser light heats the inside of a gold hohlraum to a radiation temperature of 300 eV; thermal x rays from the hohlraum interior couple to the capsule and create a central hotspot at tens of millions degrees Kelvin and a density of 100–200 g/cm3. During the laser interaction with the gold wall, m-band x rays are produced at ∼2.5 keV; these can penetrate into the capsule and preheat the ablator and DT fuel. Preheat can impact instability growth rates in the ablation front and at the fuel–ablator interface. Monitoring the hohlraum x-ray spectrum throughout the implosion is, therefore, critical; for this purpose, a Multilayer Mirror (MLM) with flat response in the 2–4 keV range has been installed in the NIF 37° Dante calorimeter. Precision engineering and x-ray calibration of components mean the channel will report 2–4 keV spectral power with an uncertainty of ±8.7%.

Rubery, M. S. (ORCID:0000000344152818)↗

DOE OSTI2022

Field Emission Mitigation in CEBAF SRF Cavities Using Deep Learning

The Continuous Electron Beam Accelerator Facility (CEBAF) operates hundreds of superconducting radio frequency (SRF) cavities in its two main linear accelerators. Field emission can occur when the cavities are set to high operating RF gradients and is an ongoing operational challenge. This is especially true in newer, higher gradient SRF cavities. Field emission results in damage to accelerator hardware, generates high levels of neutron and gamma radiation, and has deleterious effects on CEBAF operations. So, field emission reduction is imperative for the reliable, high gradient operation of CEBAF that is required by experimenters. Here we explore the use of deep learning architectures via multilayer perceptron to simultaneously model radiation measurements at multiple detectors in response to arbitrary gradient distributions. These models are trained on collected data and could be used to minimize the radiation production through gradient redistribution. This work builds on previous efforts in developing machine learning (ML) models, and is able to produce similar model performance as our previous ML model without requiring knowledge of the field emission onset for each cavity.

Ahammed, K.↗

DOE OSTI2022

SRF Cavity Instability Detection with Machine Learning at CEBAF

During the operation of 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. Identifying an unstable cavity out of the hundreds of cavities installed at CEBAF is difficult and time-consuming. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities. A fast data acquisition system for the legacy SRF cavities is being developed which samples and reports at 5 kHz to allow for detection of transients. A prototype chassis has been installed and tested in CEBAF. An autoencoder based machine learning model is being developed to identify anomalous SRF cavity behavior. The model is presently being trained on the slow (1 Hz) data that is currently available, and a separate model will be developed and trained using the fast (5 kHz) DAQ data once it becomes available. This paper will discuss the present status of the new fast data acquisition system and results of testing the prototype chassis. This paper will also detail the initial performance metrics of the autoencoder model.

Carpenter, A.↗

DOE OSTI2022

Real-Time Cavity Fault Prediction in CEBAF Using Deep Learning

Data-driven prediction of future faults is a major research area for many industrial applications. In this work, we present a new procedure of real-time fault prediction for superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF) using deep learning. CEBAF has been afflicted by frequent downtime caused by SRF cavity faults. We perform fault prediction using pre-fault RF signals from C100-type cryomodules. Using the pre-fault signal information, the new algorithm predicts the type of cavity fault before the actual onset. The early prediction may enable potential mitigation strategies to prevent the fault. In our work, we apply a two-stage fault prediction pipeline. In the first stage, a model distinguishes between faulty and normal signals using a U-Net deep learning architecture. In the second stage of the network, signals flagged as faulty by the first model are classified into one of seven fault types based on learned signatures in the data. Initial results show that our model can successfully predict most fault types 200 ms before onset. We will discuss reasons for poor model performance on specific fault types.

Rahman, M.↗

DOE OSTI2022

SRF Cavity Instability Detection with Machine Learning at CEBAF

During the operation of 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. Identifying an unstable cavity out of the hundreds of cavities installed at CEBAF is difficult and time-consuming. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities. A fast data acquisition system for the legacy SRF cavities is being developed which samples and reports at 5 kHz to allow for detection of transients. A prototype chassis has been installed and tested in CEBAF. An autoencoder based machine learning model is being developed to identify anomalous SRF cavity behavior. The model is presently being trained on the slow (1 Hz) data that is currently available, and a separate model will be developed and trained using the fast (5 kHz) DAQ data once it becomes available. This paper will discuss the present status of the new fast data acquisition system and results of testing the prototype chassis. This paper will also detail the initial performance metrics of the autoencoder model.

Carpenter, A.↗

DOE OSTI2022

Field Emission Mitigation in CEBAF SRF Cavities Using Deep Learning

The Continuous Electron Beam Accelerator Facility (CEBAF) operates hundreds of superconducting radio frequency (SRF) cavities in its two main linear accelerators. Field emission can occur when the cavities are set to high operating RF gradients and is an ongoing operational challenge. This is especially true in newer, higher gradient SRF cavities. Field emission results in damage to accelerator hardware, generates high levels of neutron and gamma radiation, and has deleterious effects on CEBAF operations. So, field emission reduction is imperative for the reliable, high gradient operation of CEBAF that is required by experimenters. Here we explore the use of deep learning architectures via multilayer perceptron to simultaneously model radiation measurements at multiple detectors in response to arbitrary gradient distributions. These models are trained on collected data and could be used to minimize the radiation production through gradient redistribution. This work builds on previous efforts in developing machine learning (ML) models, and is able to produce similar model performance as our previous ML model without requiring knowledge of the field emission onset for each cavity.

Ahammed, K.↗

DOE OSTI2022

Geranium Photodiodes for Hard Xray Detection

This report summarizes the investigations engaged at UC Davis under subcontract B632083, as related to the LLNL-LDRD Project “High-Speed X-Ray Imager Arrays.” The first section entails work done within the Electrical and Computer Engineering (ECE) Department. The purpose of the work done so far is to understand photodiode design and which parameters are conducive to the design goals, which are: I: Maximize the detector quantum efficiency. The device must have a quantum efficiency of roughly 5% at 80 keV. II: Minimize temporal response. Collection time is limited to 1ns. All charge must be collected in that time. III: Minimize dark current. In this document, various photodiode parameters and designs are explored, and their impact on these goals are tabulated. In addition to these, various instruments that have been constructed for either testing these parameters or helping teammates with their experiments. The second section entails work done with the Materials Science Engineering (MSE) Department. In accordance with the proposed development of the germanium ROIC backside imagers, a critical step in the micromachining of these devices was the removal of the substrate on which the PIN junctions were epitaxially grown. A common technique for the micromachining of silicon wafers is the use of chemical etching alongside an “etch-stop” layer. In this technique, a chemical etchant will remove material until it reaches the etch-stop layer, at which the etch rate will drop significantly. This high selectivity (ratio of etch rates) allows any unevenness in the initial etching to be effectively smoothed away in the event that part of the wafer surface reaches the etch-stop layer sooner than another. An initial literature review found a paper by Divan et al. in which the authors used ion implantation to dope germanium wafers and achieve selectivity on the order of 100, sufficient for a reliable etch-stop.

42 ENGINEERING↗

DOE OSTI2021

Superconducting Radio-frequency Cavity Fault Classification Using Machine Learning at Jefferson Laboratory

We report on the development of machine learning models for classifying C100 superconducting radiofrequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. Of the 418 SRF cavities in CEBAF, 96 are designed with a digital low-level RF system configured such that a cavity fault triggers recordings of RF signals for each of eight cavities in the cryomodule. Subject matter experts analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time - rather than postmortem - identification of the offending cavity and classification of the fault type has been implemented. We discuss the performance of the machine learning models during a recent physics run. We also discuss efforts for further insights into fault types through unsupervised learning techniques and present preliminary work on cavity and fault prediction using data collected prior to a failure event.

Tennant, C. D.↗