Related papers: Explainable Machine Learning for Breakdown Predict…
X-band accelerator structures meeting the Next Linear Collider (NLC) design requirements have been found to suffer vacuum surface damage caused by radio frequency (RF) breakdown, when processed to high electric-field gradients. Improved…
Although vacuum arcs were first identified over 110 years ago, they are not yet well understood. We have since developed a model of breakdown and gradient limits that tries to explain, in a self-consistent way: arc triggering, plasma…
We describe breakdown in 805 MHz rf accelerator cavities in terms of a number of self consistent mechanisms. We divide the breakdown process into three stages: 1) we model surface failure using molecular dynamics of fracture caused by…
Understanding the effects of RF breakdown in high-gradient accelerator structures on the accelerated beam is an extremely relevant aspect in the development of the Compact Linear Collider (CLIC) and is one of the main issues addressed at…
This review consolidates experimental, theoretical, and simulation work examining the behavior of high-field devices and the fundamental process of vacuum arc initiation, commonly referred to as breakdown. Detailed experimental observations…
Advances in Deep Learning bring further investigation into credibility and robustness, especially for safety-critical engineering applications such as the nuclear industry. The key challenges include the availability of data set (often…
High-gradient acceleration is a key research area that could enable compact linear accelerators for future colliders, light sources, and other applications. In the pursuit of high-gradient operation, RF breakdown limits the attainable…
We argue that the physics of unipolar arcs and surface cracks can help understand rf breakdown, and vacuum arc data. We outline a model of the basic mechanisms involved in breakdown and explore how the physics of unipolar arcs and cracks…
In an effort to locate the cause(s) of high electric-field breakdown in x-band accelerating structures, we have cleanly-autopsied (no debris added by post-operation structure disassembly) an RF-processed structure. Macroscopic localization…
As progress towards real implementations of cryogenic high gradient normal conducting accelerating cavities continues, a more mature understanding of the surface physics in this novel environment becomes increasingly necessary. To this end,…
Interest in air breakdown phenomena has recently been re-kindled with the advent of advanced virtual prototyping of radio frequency (RF) sources for use in high power microwave (HPM) weapons technology. Air breakdown phenomena are of…
In the field of deep learning based computer vision, the development of deep object detection has led to unique paradigms (e.g., two-stage or set-based) and architectures (e.g., Faster-RCNN or DETR) which enable outstanding performance on…
A novel quantity for predicting the high-gradient performance of radio frequency accelerating structures is presented. The quantity is motivated, derived and compared with earlier high-gradient limits and experiments. This new method models…
Fast Radio Bursts (FRBs) are millisecond-duration radio transients of extragalactic origin. Classifying repeating FRBs is essential for understanding their emission mechanisms, but remains challenging due to their short durations, high…
Analyzing vibration data using deep neural network algorithms is an effective way to detect damages in rotating machinery at an early stage. However, the black-box approach of these methods often does not provide a satisfactory solution…
While the nature of fast radio bursts (FRBs) remains unknown, population-level analyses can elucidate underlying structure in these signals. In this study, we employ deep learning methods to both classify FRBs and analyze structural…
Deep Learning has already been successfully applied to analyze industrial sensor data in a variety of relevant use cases. However, the opaque nature of many well-performing methods poses a major obstacle for real-world deployment.…
The new High Repetition Rate (HRR) CERN DC Spark System has been used to investigate the current and voltage time structure of a breakdown. Simulations indicate that vacuum breakdowns develop on ns timescales or even less. An experimental…
The use of deep learning for medical imaging has seen tremendous growth in the research community. One reason for the slow uptake of these systems in the clinical setting is that they are complex, opaque and tend to fail silently. Outside…
Artificial intelligence is increasingly integrated into radiotherapy workflows, yet such pipelines remain vulnerable to out-of-distribution image data that may introduce unexpected behavior in clinical tasks. Deep learning-based anomaly…