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C. Badiali

Publications and source records attributed to C. Badiali.

2 recordsLinked to original sources

Kilojoule-scale laser acceleration enabling efficient generation of electron-positron and muon beams

Despite many experimental attempts, laboratory pair-beam sources have not yet reached the charge, collimation, and density needed to observe kinetic pair-plasma instabilities. Here we show that an optimised direct-laser-acceleration (DLA) regime in a gas target can close this gap in the regime of kilojoule-class petawatt lasers. Quasi-3D PIC and Geant4 simulations show that DLA-accelerated electron bunches carry tens of nC at GeV-scale energies with low divergence. Converted in a high-Z target, they generate up to 3x10^12 positrons with tunable energy spectra and transverse sizes of several skin depths in the beam rest frame. A proof-of-principle PIC simulation of the pair beam propagating through background gas shows the growth of a current-filamentation instability. This provides direct numerical evidence that kinetic pair dynamics are within reach of near-term facilities such as ELI-L4. The same electron driver also generates 6.5x10^5 Bethe-Heitler muons per shot, at up to 580 muons/J. We derive a scaling law for the muon yield and validate it against Geant4 simulation. Above a modest driver-energy threshold, the yield depends on the total energy delivered to the electron beam rather than on its peak energy - a design principle for future laser-based muon sources. These results establish DLA-driven secondary sources as a practical, near-term pathway to laboratory pair plasmas, high-yield muon beams, and lepton-accelerator injectors.

physics.plasm-ph

Efficiency Parameterization with Neural Networks

Multidimensional efficiency maps are commonly used in high energy physics experiments to mitigate the limitations in the generation of large samples of simulated events. Binned multidimensional efficiency maps are however strongly limited by statistics. We propose a neural network approach to learn ratios of local densities to estimate in an optimal fashion efficiencies as a function of a set of parameters. Graph neural network techniques are used to account for the high dimensional correlations between different physics objects in the event. We show in a specific toy model how this method is applicable to produce accurate multidimensional efficiency maps for heavy flavor tagging classifiers in HEP experiments, including for processes on which it was not trained.

hep-ex