Description
Advancements in all-optical experimental setups, such as increased repetition rates and enhanced control in the accelerator and scattering stages, are enabling studies with greater statistical significance in strong-field quantum electrodynamics (SFQED).
Future experiments will allow more accurate testing of theoretical models and the development of novel high- energy particle and radiation sources. The ability to extract more comprehensive information from experimental data is then essential for refining our understanding of SFQED phenomena. Rather than relying on isolated diagnostics, analyzing correlated energy-angle distributions of both electrons and emitted photons within the same interaction event provides a more accurate and complete comparison with theoretical predictions.
Specifically, reliably identifying and separating from the background signal signatures of radiation reaction on the electron beam after collision with an intense laser is one of the current main challenges of the field.
To facilitate this, we have developed a computational framework to model expected outcomes in strong-field QED scattering experiments [1]. The approach enhances our ability to interpret complex experimental data, particularly in scenarios where multiple physical effects could influence the observed distributions.
We validate this new tool through comparisons with particle-in-cell simulations incorporating Monte Carlo methods and existing theoretical predictions. The model is applied across a broad range of interaction conditions, including variations in laser focusing, electron beam divergence, and different collision geometries, such as head-on, oblique, and perpendicular configurations. These comparisons provide insights into the influence of key experimental factors on the observed radiation and particle distributions.
By improving the accuracy of data interpretation and comparison with theoretical models, this approach contributes to ongoing efforts to deepen our understanding of strong-field QED phenomena in high-intensity laser experiments.
References:
[1] Amaro, Ó, and Vranic, M., QScatter: numerical framework for fast prediction of particle distributions in electron-laser scattering, Plasma Phys. Control. Fusion 66 045006 (2024)
