scholarly journals High-Brightness Continuous-Wave Electron Beams from Superconducting Radio-Frequency Photoemission Gun

2020 ◽  
Vol 124 (24) ◽  
Author(s):  
I. Petrushina ◽  
V. N. Litvinenko ◽  
Y. Jing ◽  
J. Ma ◽  
I. Pinayev ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Continuous Wave ◽  
Electron Beams ◽  
High Brightness ◽  
Superconducting Radio Frequency

scholarly journals Long lifetime of bialkali photocathodes operating in high gradient superconducting radio frequency gun

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
E. Wang ◽  
V. N. Litvinenko ◽  
I. Pinayev ◽  
M. Gaowei ◽  
J. Skaritka ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Electron Beams ◽  
Spot Size ◽  
Laser Spot ◽  
Laser Spot Size ◽  
High Quantum Efficiency ◽  
High Brightness ◽  
High Charge ◽  
Superconducting Radio Frequency ◽  
Long Lifetime

AbstractHigh brightness, high charge electron beams are critical for a number of advanced accelerator applications. The initial emittance of the electron beam, which is determined by the mean transverse energy (MTE) and laser spot size, is one of the most important parameters determining the beam quality. The bialkali photocathodes illuminated by a visible laser have the advantages of high quantum efficiency (QE) and low MTE. Furthermore, Superconducting Radio Frequency (SRF) guns can operate in the continuous wave (CW) mode at high accelerating gradients, e.g. with significant reduction of the laser spot size at the photocathode. Combining the bialkali photocathode with the SRF gun enables generation of high charge, high brightness, and possibly high average current electron beams. However, integrating the high QE semiconductor photocathode into the SRF guns has been challenging. In this article, we report on the development of bialkali photocathodes for successful operation in the SRF gun with months-long lifetime while delivering CW beams with nano-coulomb charge per bunch. This achievement opens a new era for high charge, high brightness CW electron beams.

Development of a 1497-MHz, 13.5-kW Continuous-Wave Magnetron for Superconducting Radio Frequency Accelerator

2021 ◽  
pp. 1-9
Author(s):  
Wenliang Li ◽  
Pengjiao Zhang ◽  
Bowen Zhou ◽  
Hong Zhang ◽  
Youchun Liu ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Continuous Wave ◽  
Superconducting Radio Frequency

scholarly journals Radiation Generation with an Existing Demonstrator of an Energy-Recovery Continuous-Wave Superconducting RF Accelerator

2020 ◽  
Vol 77 (5) ◽  
pp. 337-343
Author(s):  
Ji-Gwang Hwang ◽  
Michael Abo-Bakr ◽  
Aleksandr Matveenko ◽  
Georgios Kourkafas ◽  
Thorsten Kamps
Keyword(s):  
Energy Recovery ◽  
Continuous Wave ◽  
High Brightness ◽  
X Ray ◽  
Superconducting Radio Frequency ◽  
Energy Recovery Linac ◽  
Radiation Generation ◽  
Superconducting Rf ◽  
Current Electron ◽  
Average Current

Abstract Over the past decades, many accelerator laboratories have put much effort into the development of compact energy-recovery linac (ERL) demonstrators to verify various physical and technical aspects of the generation, acceleration, transport and energy recovery of high brightness and high average current electron beams in a superconducting radio-frequency (SRF) linear accelerator. Beyond these goals, the ERL demonstrator also offers unique opportunities to study novel schemes for THz and X-ray radiation generation. In this paper, we discuss feasible options for schemes generating THz and X-ray radiation at low-energy continuous-wave (CW) SRF ERL demonstrators such as the bERLinPro accelerator.

scholarly journals Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory

2022 ◽  
Vol 4 ◽  
Author(s):  
Lasitha Vidyaratne ◽  
Adam Carpenter ◽  
Tom Powers ◽  
Chris Tennant ◽  
Khan M. Iftekharuddin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Learning ◽  
Radio Frequency ◽  
Large Scale ◽  
Continuous Wave ◽  
Time Series Data ◽  
Fault Classification ◽  
Series Data ◽  
Superconducting Radio Frequency

This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of origin. This information is subsequently utilized to identify failure trends and to implement corrective measures on the offending cavity. Manual inspection of large-scale, time-series data, generated by frequent system failures is tedious and time consuming, and thereby motivates the use of machine learning (ML) to automate the task. This study extends work on a previously developed system based on traditional ML methods (Tennant and Carpenter and Powers and Shabalina Solopova and Vidyaratne and Iftekharuddin, Phys. Rev. Accel. Beams, 2020, 23, 114601), and investigates the effectiveness of deep learning approaches. The transition to a DL model is driven by the goal of developing a system with sufficiently fast inference that it could be used to predict a fault event and take actionable information before the onset (on the order of a few hundred milliseconds). Because features are learned, rather than explicitly computed, DL offers a potential advantage over traditional ML. Specifically, two seminal DL architecture types are explored: deep recurrent neural networks (RNN) and deep convolutional neural networks (CNN). We provide a detailed analysis on the performance of individual models using an RF waveform dataset built from past operational runs of CEBAF. In particular, the performance of RNN models incorporating long short-term memory (LSTM) are analyzed along with the CNN performance. Furthermore, comparing these DL models with a state-of-the-art fault ML model shows that DL architectures obtain similar performance for cavity identification, do not perform quite as well for fault classification, but provide an advantage in inference speed.

scholarly journals An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning

2020 ◽  
Vol 10 (19) ◽  
pp. 6885
Author(s):  
Sahar Ujan ◽  
Neda Navidi ◽  
Rene Jr Landry
Keyword(s):  
Feature Extraction ◽  
Radio Frequency ◽  
Transfer Learning ◽  
Communication Networks ◽  
Continuous Wave ◽  
Critical Role ◽  
Video Stream ◽  
Radio Frequency Interference ◽  
Wave Interference ◽  
Continuous Wave Interference

Radio Frequency Interference (RFI) detection and characterization play a critical role in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable complicated feature extraction step in ML, we propose an efficient Deep Learning (DL)-based methodology using transfer learning to determine both the type of received signals and their modulation type. To this end, the scalogram of the received signals is used as the input of the pretrained convolutional neural networks (CNN), followed by a fully-connected classifier. This study considers a digital video stream as the signal of interest (SoI), transmitted in a real-time satellite-to-ground communication using DVB-S2 standards. To create the RFI dataset, the SoI is combined with three well-known jammers namely, continuous-wave interference (CWI), multi- continuous-wave interference (MCWI), and chirp interference (CI). This study investigated four well-known pretrained CNN architectures, namely, AlexNet, VGG-16, GoogleNet, and ResNet-18, for the feature extraction to recognize the visual RFI patterns directly from pixel images with minimal preprocessing. Moreover, the robustness of the proposed classifiers is evaluated by the data generated at different signal to noise ratios (SNR).

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