Related papers: Integration of Machine Learning-Based Plasma Accel…
Particle accelerator modeling is an important field of research and development, essential to investigating, designing and operating some of the most complex scientific devices ever built. Kinetic simulations of relativistic, charged…
In this dissertation, a fully object-oriented, fully relativistic, multi-dimensional Particle-In-Cell code was developed and applied to answer key questions in plasma-based accelerator research. The simulations increase the understanding of…
Computer aided engineering of multi-time-scale plasma systems which exhibit a quasi-steady state solution are challenging due to the large number of time steps required to reach convergence. Machine learning techniques combined with…
The field of plasma-based particle accelerators has seen tremendous progress over the past decade and experienced significant growth in the number of activities. During this process, the involved scientific community has expanded from…
Plasma wakefield acceleration is a groundbreaking technique for accelerating particles, capable of sustaining gigavolt-per-meter accelerating fields. Understanding the physical mechanisms governing the recovery of plasma accelerating…
Electromagnetic processes of charged particles interaction with oriented crystals provide a wide variety of innovative applications such as beam steering, crystal-based extraction/collimation of leptons and hadrons in an accelerator, a…
Designing a high-quality plasma injector electron source driven by a laser beam relies on numerical parametric studies using particle-in-cell codes. The common input parameters to explore are laser characteristics, plasma species and…
Plasma wakefield acceleration holds remarkable promise for future advanced accelerators. The design and optimization of plasma-based accelerators typically require particle-in-cell simulations, which can be computationally intensive and…
We explore the idea of integrating machine learning (ML) with high performance computing (HPC)-driven simulations to address challenges in using simulations to teach computational science and engineering courses. We demonstrate that a ML…
Machine learning (ML) provides a broad spectrum of tools and architectures that enable the transformation of data from simulations and experiments into useful and explainable science, thereby augmenting domain knowledge. Furthermore,…
Self-guided femtosecond laser pulses propagating in low-pressure gas can generate plasma filaments, establishing a new framework for plasma wakefield acceleration. Unlike conventional schemes relying on mechanically confined or preformed…
Machine learning (ML) is a subfield of artificial intelligence. The term applies broadly to a collection of computational algorithms and techniques that train systems from raw data rather than a priori models. ML techniques are now…
We explore the applications of machine learning techniques in relativistic laser-plasma experiments beyond optimization purposes. We predict the beam charge of electrons produced in a laser wakefield accelerator given the laser wavefront…
The Geant4 toolkit is the leading software for the simulation of particle transport through matter, widely used in nuclear physics, high-energy physics, and medical physics. However, the initial learning curve for new developers can be…
All-optical controlled injection and acceleration of electrons in a laser wakefield has recently been achieved (Faure et al, Nature v. 444 p. 737, 2006). Injection was made using a second counterpropagating laser pulse with the same…
We propose a Plasma Accelerator Research Station (PARS) based at proposed FEL test facility CLARA (Compact Linear Accelerator for Research and Applications) at Daresbury Laboratory. The idea is to use the relativistic electron beam from…
We present numerical simulations results on the injection and acceleration of a 10 MeV, 10 pC electrons beam in a plasma wave generated in a gas cell by a 2J, 45 fs laser beam. This modeling is related to the ESCULAP project in which the…
To improve the performance-critical stability and brightness of the electron bunch at injection into the proton-driven plasma wakefield at the AWAKE CERN experiment, automation approaches based on unsupervised Machine Learning (ML) were…
The dynamic process of a laser or particle beam propagating from vacuum into underdense plasma has been investigated theoretically. Our theoretical model combines a Lagrangian fluid model with the classic quasistatic wakefield theory. It is…
Complex phenomena are generally modeled with sophisticated simulators that, depending on their accuracy, can be very demanding in terms of computational resources and simulation time. Their time-consuming nature, together with a typically…