Related papers: Integration of Machine Learning-Based Plasma Accel…
Plasma-based accelerators (PBAs) driven by either intense lasers (laser wakefield accelerators, LWFAs) or particle beams (plasma wakefield accelerators, PWFAs), can accelerate charged particles at extremely high gradients compared to…
Machine learning (ML) based interatomic potentials are emerging tools for materials simulations but require a trade-off between accuracy and speed. Here we show how one can use one ML potential model to train another: we use an existing,…
We present a numerical study of Gamma-Ray Burst - Circumburst Medium interaction and plasma preconditioning via Compton scattering. The simulation tool employed is a unique hybrid model; it combines a highly parallelized (Vlasov)…
Quantum-accurate computer simulations play a central role in understanding phase-change materials (PCMs) for advanced memory technologies. However, direct quantum-mechanical simulations are necessarily limited to simplified models,…
The paper presents a study of wakefield generation and electron injection via propagation of radially polarized laser pulses in homogeneous pre-ionized plasma. The analytical study is based on Lorentz force and continuity equations.…
Machine-learning (ML) models in flow cytometry have the potential to reduce error rates, increase reproducibility, and boost the efficiency of clinical labs. While numerous ML models for flow cytometry data have been proposed, few studies…
Autoresonant phase-locking of the plasma wakefield to the beat frequency of two driving lasers offers advantages over conventional wakefield acceleration methods, since it requires less demanding laser parameters and is robust to variations…
Theoretical computation of cosmological observables is an intensive process, restricting the speed at which cosmological data can be analysed and cosmological models constrained, and therefore limiting research access to those with high…
Laser and beam driven wakefields promise orders of magnitude increases in electric field gradients for particle accelerators for future applications. Key areas to explore include the emittance properties of the generated beams and…
Model-based plasma scenario development lies at the heart of the design and operation of future fusion powerplants. Including turbulent transport in integrated models is essential for delivering a successful roadmap towards operation of…
Plasma wakefield accelerators (PWFA) represent one of the promising new accelerator concepts that are now being developed intensively for future applications in high-energy physics and industry. Among the unresolved problems of practical…
Understanding dense matter hydrodynamics is critical for predicting plasma behavior in environments relevant to laser-driven inertial confinement fusion. Traditional diagnostic sources face limitations in brightness, spatiotemporal…
Machine learning (ML) is a rapidly growing area of research in the field of particle physics, with a vast array of applications at the CERN LHC. ML has changed the way particle physicists conduct searches and measurements as a versatile…
Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or be…
When modeling laser wakefield acceleration (LWFA) using the particle-in-cell (PIC) algorithm in a Lorentz boosted frame, the plasma is drifting relativistically at $\beta_b c$ towards the laser, which can lead to a computational speedup of…
We demonstrate a tandem lens optical setup, comprising two diffractive optics, that focuses a high-power ultrafast laser with a shaped on-axis intensity profile, producing a meter-long Bessel focus. The intended use of the optical setup is…
Plasma wakefield acceleration (PWFA) holds much promise for advancing the energy frontier because it can potentially provide a 1000-fold or more increase in acceleration gradient with excellent power efficiency in respect with standard…
Simulating laser wakefield acceleration (LWFA) in a Lorentz boosted frame in which the plasma drifts towards the laser with $v_b$ can speedup the simulation by factors of $\gamma^2_b=(1-v^2_b/c^2)^{-1}$. In these simulations the…
Simulating the time evolution of physical systems is pivotal in many scientific and engineering problems. An open challenge in simulating such systems is their multi-resolution dynamics: a small fraction of the system is extremely dynamic,…
Addressing the charged particle beam diagnostics in accelerators poses a formidable challenge, demanding high-fidelity simulations in limited computational time. Machine learning (ML) based surrogate models have emerged as a promising tool…