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Measurement of bunch length and temporal distribution using accelerating radio frequency cavity in low-emittance injector.

ABSTRACT: We demonstrate an experimental methodology for measuring the temporal distribution of pico-second level electron bunch with low energy using radial electric and azimuthal magnetic fields of an accelerating ([Formula: see text] mode) radio frequency (RF) cavity that is used for accelerating electron beams in a linear accelerator. In this new technique, an accelerating RF cavity provides a phase-dependent transverse kick to the electrons, resulting in the linear coupling of the trajectory angle with the longitudinal position inside the bunch. This method does not require additional devices on the beamline since it uses an existing accelerating cavity for the projection of the temporal distribution to the transverse direction. We present the theoretical basis of the proposed method and validate it experimentally in the compact-energy recovery linac accelerator at KEK. Measurements were demonstrated using a 2-cell superconducting booster cavity with a peak on-axis accelerating field ([Formula: see text]) of 7.21 MV/m.

SUBMITTER: Hwang JG

PROVIDER: S-EPMC7609657 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Measurement of bunch length and temporal distribution using accelerating radio frequency cavity in low-emittance injector.

Hwang Ji-Gwang JG Miyajima Tsukasa T Honda Yosuke Y Kim Eun-San ES

Scientific reports 20201103 1

We demonstrate an experimental methodology for measuring the temporal distribution of pico-second level electron bunch with low energy using radial electric and azimuthal magnetic fields of an accelerating ([Formula: see text] mode) radio frequency (RF) cavity that is used for accelerating electron beams in a linear accelerator. In this new technique, an accelerating RF cavity provides a phase-dependent transverse kick to the electrons, resulting in the linear coupling of the trajectory angle wi ...[more]

PMID: 33144680

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Project description: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.

Dataset Information

Measurement of bunch length and temporal distribution using accelerating radio frequency cavity in low-emittance injector.

Publications

Measurement of bunch length and temporal distribution using accelerating radio frequency cavity in low-emittance injector.

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