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O’Shea, Brendan

Publications and source records attributed to O’Shea, Brendan.

Probing strong-field QED in beam-plasma collisions

Ongoing progress in laser and accelerator technology opens new possibilities in high-field science, notably to investigate the largely unexplored strong-field quantum electrodynamics (SFQED) regime where electron-positron pairs can be created directly from light-matter or even light-vacuum interactions. Laserless strategies such as beam-beam collisions have also been proposed to access the nonperturbative limit of SFQED. Here we report on a concept to probe SFQED by harnessing the interaction between a high-charge, ultrarelativistic electron beam and a solid conducting target. When impinging onto the target surface, the beam self fields are reflected, partly or fully, depending on the beam shape; in the rest frame of the beam electrons, these fields can exceed the Schwinger field, thus triggering SFQED effects such as quantum nonlinear inverse Compton scattering and nonlinear Breit-Wheeler electron-positron pair creation. Through reduced modeling and kinetic numerical simulations, we show that this single-beam setup can achieve interaction conditions similar to those envisioned in beam-beam collisions, but in a simpler and more controllable way owing to the automatic overlap of the beam and driving fields. This scheme thus eases the way to precision studies of SFQED and is also a promising milestone towards laserless studies of nonperturbative SFQED.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Virtual Diagnostic Suite for Electron Beam Prediction and Control at FACET-II

We discuss the implementation of a suite of virtual diagnostics at the FACET-II facility currently under commissioning at SLAC National Accelerator Laboratory. The diagnostics will be used for the prediction of the longitudinal phase space along the linac, spectral reconstruction of the bunch profile, and non-destructive inference of transverse beam quality (emittance) while using edge radiation at the injector dogleg and bunch compressor locations. These measurements will be folded into adaptive feedbacks and Machine Learning (ML)-based reinforcement learning controls to improve the stability and optimize the performance of the machine for different experimental configurations. In this paper we describe each of these diagnostics with expected measurement results that are based on simulation data and discuss progress towards implementation in regular operations.

Emma, Claudio (ORCID:0000000247191831)↗