Related papers: Integration of Machine Learning-Based Plasma Accel…
Due to its flexible architecture, FPGAs support unique, deep hardware pipeline implementations for accelerating HPC applications. However, these devices are quite new in the HPC space, and thus, have been scarcely explored outside some…
A method for the optimisation and advanced studies of a laser-plasma electron injector is presented, based on a truncated ionisation injection scheme for high quality beam production. The SMILEI code is used with laser envelope…
We present a GPU-accelerated method for muon transport based on histogram sampling that delivers orders of magnitude faster performance than CPU-based Geant4 simulation. Our method employs precomputed histograms of momentum loss and…
We present a conceptual design for a hybrid laser-to-beam-driven plasma wakefield accelerator. In this setup, the output beams from a laser-driven plasma wakefield accelerator (LWFA) stage are used as input beams of a new beam-driven plasma…
Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development. The enormous complexity involved in existing multi-variable synthesis methods…
Wakefield acceleration methods are known due to some their advantages. The main of them is the high accelerating gradient up to several teravolts per meter. In the paper another important advantage is concluded to the possibility of using a…
Laser-plasma accelerators offer a compact means of producing high-energy electron beams, but their performance is fundamentally limited by dephasing between the accelerated electrons and the plasma wave. To overcome this limitation, we…
In the present work, we demonstrate for the first time a proof-of-principle experiment for nanoparticle-assisted laser wakefield acceleration. The nanoparticles, generated through laser ablation of aluminium, were introduced into the plasma…
Particle Accelerators are high power complex machines. To ensure uninterrupted operation of these machines, thousands of pieces of equipment need to be synchronized, which requires addressing many challenges including design, optimization…
Machine Learning (ML) models are trained using historical data to classify new, unseen data. However, traditional computing resources often struggle to handle the immense amount of data, commonly known as Big Data, within a reasonable time…
Machine learning is offering powerful new tools for the development and discovery of reduced models of nonlinear, multiscale plasma dynamics from the data of first-principles kinetic simulations. However, ensuring the physical consistency…
Researchers in the field of ultra-intense laser science are beginning to embrace machine learning methods. In this study we consider three different machine learning methods -- a two-hidden layer neural network, Support Vector Regression…
The current activity of the SPARC_LAB test-facility is focused on the realization of plasma-based acceleration experiments with the aim to provide accelerating field of the order of several GV/m while maintaining the overall quality (in…
A Geant4 based simulation platform of the Holland Proton Therapy Centre (HollandPTC, Netherlands) R&D beamline (G4HPTC-R&D) was developed to enable the planning, optimisation and advanced dosimetry for radiobiological studies. It…
A computationally efficient and accurate machine-learned (ML) interatomic potential is developed for Ti$_{n+1}$C$_n$ MXenes. With a diverse set of structures computed with density functional theory, the trained ML potential demonstrates…
We have developed a deep generative model that can produce accurate optical emission spectra and colour images of an ICP plasma using only the applied coil power, electrode power, pressure and gas flows as inputs -- essentially an empirical…
We revisit the matching conditions for self-guided laser pulse propagation in plasma and refine their formulation to maximize the energy of electrons produced via laser wakefield acceleration. Bayesian optimization, combined with…
SPARC_LAB is a facility designed for the production of FEL radiation and the exploration of advanced acceleration techniques using a high brightness electron photo-injector. Specifically, particle-driven plasma wakefield acceleration (PWFA)…
The prediction of product translational, vibrational, and rotational energy distributions for arbitrary initial conditions for reactive atom+diatom collisions is of considerable practical interest in atmospheric re-entry. Due to the large…
Next-generation plasma-based accelerators can push electron bunches to gigaelectronvolt energies within centimetre distances. The plasma, excited by a driver pulse, generates large electric fields that can efficiently accelerate a trailing…