Related papers: Integration of Machine Learning-Based Plasma Accel…
Advances in ultra-intense laser technology have increased repetition rates and average power for chirped-pulse laser systems, which offers a promising solution for many applications including energetic proton sources. An important challenge…
The ELIMED (MEDical and multidisciplinary application at ELI Beamlines) beam line is being developed at INFN-LNS with the aim of transporting and selecting in energy proton and ion beams accelerated by laser-matter interaction at ELI…
Experimental results, supported by precise modelling, demonstrate optimisation of a plasma-based injector with intermediate laser pulse energy ($<1$ J), corresponding to a normalised vector potential $a_0 = 2.15$, using ionisation injection…
While discrete-event simulators are essential tools for architecture research, design, and development, their practicality is limited by an extremely long time-to-solution for realistic applications under investigation. This work describes…
High-fidelity physics simulations are powerful tools in the design and optimization of charged particle accelerators. However, the computational burden of these simulations often limits their use in practice for design optimization and…
A surrogate model for particle-in-cell plasma simulations based on a graph neural network is presented. The graph is constructed in such a way as to enable the representation of electromagnetic fields on a fixed spatial grid. The model is…
The accurate modeling of plasma-based accelerators relies on costly numerical simulations due to the complexity of laser-plasma and beam-plasma interactions. Several strategies can highly reduce the computational cost compared to 3D…
An electron injector concept for laser-plasma accelerator was developed in ref [1] and [2] ; it relies on the use of counter-propagating ultrashort laser pulses. In [2], the scheme is as follows: the pump laser pulse generates a large…
The interaction of the witness beam with the surrounding plasma particles and wakefields was studied. The impli- cations of the elastic scattering process on beam emittance and, emittance evolution under the focusing and acceleration…
The resources needed for Particle in Cell simulations of Laser Wakefield Acceleration can be greatly reduced in many cases of interest using an envelope model. However, the inclusion of tunneling ionization in this time averaged treatment…
High-intensity laser plasma interactions create complex computational problems because they involve both fluid and kinetic regimes, which need models that maintain physical precision while keeping computational speed. The research…
Geant4 is a tool kit developed by a collaboration of physicists and computer professionals in the High Energy Physics field for simulation of the passage of particles through matter. The motivation for the development of the Beam Tools is…
As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs.…
Recent advancements in Machine Learning (ML) have substantially improved its predictive and computational abilities, offering promising opportunities for surrogate modeling in scientific applications. By accurately approximating complex…
Machine learning is applied to investigate the phase transition of two-dimensional complex plasmas. The Langevin dynamics simulation is employed to prepare particle suspensions in various thermodynamic states. Based on the resulted particle…
Modeling of laser-plasma wakefield accelerators in an optimal frame of reference \cite{VayPRL07} is shown to produce orders of magnitude speed-up of calculations from first principles. Obtaining these speedups requires mitigation of a…
A novel approach for positron injection and acceleration in laser driven plasma wakefield is proposed. A theoretical model is developed and confirmed through PIC simulation. One ring-shaped beam and one co-axially propagating Gaussian beam…
The acceleration of charged particles is fundamental not only for experimental studies in particle physics but also for applications in fields such as semiconductor manufacturing and medical therapies. However, conventional accelerators…
Invertible neural networks are a recent technique in machine learning promising neural network architectures that can be run in forward and reverse mode. In this paper, we will be introducing invertible surrogate models that approximate…
Plasma wakefield accelerators are capable of sustaining gigavolt-per-centimeter accelerating fields, surpassing the electric breakdown threshold in state-of-the-art accelerator modules by 3-4 orders of magnitude. Beam-driven wakefields…