In physics research particle accelerators are highly valued, and extraordinarily expensive, technical instruments. The high cost of particle accelerators results from the immense lengths required to accelerate particles to high energies, using radio frequency cavities. A current promising field of research, laser-driven particle acceleration has the potential to reduce the size, cost, and energy consumption of particle accelerators by orders of magnitude. To understand and control particle acceleration in plasmas using ultra-small spatial configurations, researchers have been developing computational models to simulate the acceleration environment. Within these models, computational scientists have introduced virtual diagnostics to serve as the digital parallel to experimental detectors. Using WarpX\cite{Vay:2021}, an advanced Particle-in-Cell code that simulates laser-driven particle acceleration, we have developed a virtual diagnostic to measure electromagnetic radiation. Such radiation can for instance be produced from scattered and transmitted laser beams. This \textit{FieldProbe} diagnostic measures individual field components for electric and magnetic fields at a point or at all points along a specified geometry (line or plane). This diagnostic is GPU-accelerated and parallelized using the Message Passing Interface (MPI) and can thus run on High Performance Computing Centers such as NERSC.
Turning the current experimental plasma accelerator state-of-the-art from a promising technology into mainstream scientific tools depends critically on high-performance, high-fidelity modeling of complex processes that develop over a wide range of space and time scales. As part of the U.S. Department of Energy's Exascale Computing Project, a team from Lawrence Berkeley National Laboratory, in collaboration with teams from SLAC National Accelerator Laboratory and Lawrence Livermore National Laboratory, is developing a new plasma accelerator simulation tool that will harness the power of future …
Virtual Diagnostic (VD) is a computational tool based on deep learning that can be used to predict a diagnostic output. VDs are especially useful in systems where measuring the output is invasive, limited, costly or runs the risk of altering the output. Given a prediction, it is necessary to relay how reliable that prediction is, i.e. quantify the uncertainty of the prediction. In this paper, we use ensemble methods and quantile regression neural networks to explore different ways of creating and analyzing prediction's uncertainty on experimental data from the Linac Coherent Light Source at SL…
At present, a variety of image-based diagnostics are used in particle accelerator systems. Often times, these are viewed by a human operator who then makes appropriate adjustments to the machine. Given recent advances in using convolutional neural networks (CNNs) for image processing, it should be possible to use image diagnostics directly in control routines (NN-based or otherwise). This is especially appealing for non-intercepting diagnostics that could run continuously during beam operation. Here, we show results of a first step toward implementing such a controller: our trained CNN can pre…
After a summary of relevant comments and recommendations from various reports over the last ten years, this paper examines the modeling needs in accelerator physics, from the modeling of single beams and individual accelerator elements, to the realization of virtual twins that replicate all the complexity to model a particle accelerator complex as accurately as possible. We then discuss cutting-edge and emerging computing opportunities, such as advanced algorithms, AI/ML and quantum computing, computational needs in hardware, software performance, portability and scalability, and needs for sca…
The operation of Ultrafast Electron Diffractometers (UEDs) and Free-Electron Lasers (FELs) relies on high-brightness electron beams produced by radio-frequency (RF) photocathode guns. The next generation of high-repetition rate UEDs and FELs requires electron beams with a high average brightness. To this end, we introduce a continuous wave RF photocathode gun at 325 MHz with an APEX-like geometry. The gun allows for the production of electron beams with very high both peak and average 5D brightness while having moderate RF power consumption. The gun is operated in blowout regime with an energy…
As charged particle bunches become shorter and more intense, the effects of nonlinear intra-bunch collective interactions such as space charge forces and bunch-to-bunch influences such as wakefields and coherent synchrotron radiation also increase. Shorter more intense bunches are also more difficult to accurately image because their dimensions are beyond the resolution of existing diagnostics and they may be destructive to intercepting diagnostics. The limited availability of detailed diagnostics for intense high energy beams is a fundamental challenge for the accelerator community because bo…
Electron cloud beam instabilities are an important consideration in virtually all high-energy particle accelerators and could pose a formidable challenge to forthcoming high-intensity accelerator upgrades. Dedicated tests have shown beampipe coatings dramatically reduce the density of electron cloud in particle accelerators. In this work, we evaluate the performance of titanium nitride, amorphous carbon, and diamond-like carbon as beampipe coatings for the mitigation of electron cloud in the Fermilab Main Injector. Altogether our tests represent 2700 ampere-hours of proton operation spanning f…
SLAC National Accelerator Laboratory（SLAC国家加速器实验室）
Longitudinal phase space (LPS) provides a critical information about electron beam dynamics for various scientific applications. For example, it can give insight into the high-brightness X-ray radiation from a free electron laser. Existing diagnostics are invasive, and often times cannot operate at the required resolution. In this work we present a machine learning-based Virtual Diagnostic (VD) tool to accurately predict the LPS for every shot using spectral information collected non-destructively from the radiation of relativistic electron beam. We demonstrate the tool's accuracy for three di…
SLAC National Accelerator Laboratory（SLAC国家加速器实验室）；University of Oxford（牛津大学）
Virtual Diagnostic (VD) is a deep learning tool that can be used to predict a diagnostic output. VDs are especially useful in systems where measuring the output is invasive, limited, costly or runs the risk of damaging the output. Given a prediction, it is necessary to relay how reliable that prediction is. This is known as 'uncertainty quantification' of a prediction. In this paper, we use ensemble methods and quantile regression neural networks to explore different ways of creating and analyzing prediction's uncertainty on experimental data from the Linac Coherent Light Source at SLAC. We ai…
SPIRAL2 is a superconducting accelerator for protons, deuterons and heavy ions delivering a maximum beam power of 200 kW at 40 MeV (for deuteron beams). 26 superconducting quarter wave cavities are operated at 4.4 K, plunged in a liquid helium bath with a drastic phase separator pressure control. Previous years have seen the development of advanced process control for cryogenics allowing to cope with high heat load dynamics thanks to an automatic heat dissipation compensation and a model based control. The latter is based on models, using the Simcryogenics library, optimized and linearised in …
