arXiv subjects

Chiara Badiali

Publications and source records attributed to Chiara Badiali.

4 recordsLinked to original sources

arXiv2026

Simulation Design for Velocity-Controlled Spatio-Temporal Drivers in Laser Wakefield Acceleration

Velocity-controlled spatio-temporal (ST) laser drivers offer a route to tailoring laser-plasma interactions by allowing the velocity of the intensity peak to be controlled independently of the envelope group velocity. In this work, we present a simulation-design workflow for PIC modelling of subluminal velocity-controlled ST pulses in OSIRIS based on a Maxwell-consistent spectral construction expressed as a superposition of exact vacuum solutions, and we describe its discrete k-space representation for numerical initialisation. We then examine wakefield excitation with velocity-controlled drivers, showing how the ST geometry couples the effective longitudinal extent of the high-intensity region to the transverse scale and deriving scaling guidelines for near-resonant excitation in the subluminal regime. Finally, we discuss the geometric constraints that make long-distance simulations costly, including focus-envelope slippage and strong transverse expansion, and we show that continuous wall injection can reproduce the intended vacuum propagation while substantially reducing the transverse domain size. Together, these results provide practical guidelines for accurate and computationally efficient PIC simulations of velocity-controlled ST drivers in wakefield-relevant regimes.

physics.plasm-ph↗

arXiv2025

Plasma Accelerator For Decaying Particles

We introduce a plasma wakefield acceleration scheme capable of boosting initially subrelativistic particles to relativistic velocities within millimeter-scale distances. A subluminal light pulse drives a wake whose velocity is continuously matched to the beam speed through a tailored plasma density, thereby extending the dephasing length. We develop a theoretical model that is generalizable across particle mass, initial velocity, and the particular accelerating bucket being used, and we verify its accuracy with particle-in-cell simulations using laser drivers with energies in the Joule range.

physics.plasm-ph↗

arXiv2024

Neural network sampling of Bethe-Heitler process in particle-in-cell codes

This study uses neural networks to improve Monte Carlo (MC) implementations of the Bethe-Heitler process in Particle-In-Cell (PIC) codes. We provide a neural network that is as accurate as pre-calculated tables, and requires a hundred times less memory to store. It is trained to predict Bethe-Heitler pair production cross-sections for atomic numbers 1-50 and photon energies between 1 MeV and 10 GeV in the PIC code OSIRIS. We first validate our approach against a theoretical estimate in a simplified context. We later prove that both approaches have similar performance in a typical relativistic laser-plasma interaction scenario. The large memory decrease accessible with neural networks will enable introducing more advanced cross-section models for Bethe-Heitler pair production and other QED mechanisms in the MC modules of PIC codes.

physics.comp-ph↗

arXiv2022

Machine Learning-based models in particle-in-cell codes for advanced physics extensions

In this paper we propose a methodology for the efficient implementation of Machine Learning (ML)-based methods in particle-in-cell (PIC) codes, with a focus on Monte-Carlo or statistical extensions to the PIC algorithm. The presented approach allows for neural networks to be developed in a Python environment, where advanced ML tools are readily available to proficiently train and test them. Those models are then efficiently deployed within highly-scalable and fully parallelized PIC simulations during runtime. We demonstrate this methodology with a proof-of-concept implementation within the PIC code OSIRIS, where a fully-connected neural network is used to replace a section of a Compton scattering module. We demonstrate that the ML-based method reproduces the results obtained with the conventional method and achieves better computational performance. These results offer a promising avenue for future applications of ML-based methods in PIC, particularly for physics extensions where an ML-based approach can provide a higher performance increase.

physics.comp-ph↗