Plasma Physics (physics.plasm-ph)

Abstract: Plasma acceleration is considered a prospective technology for building a compact multi-TeV electron-positron collider in the future. The challenge of this endeavor is greater for positrons than for the electrons because usually the self-generated fields from laser-plasma interaction are not well-suited for positron focusing and on-axis guiding. In addition, an external positron source is required, while electrons are naturally available in the plasma. Here, we study electron-positron pair generation by an orthogonal collision of a multi-PW laser pulse and a GeV electron beam by the nonlinear Breit-Wheeler process. We studied conditions favorable for positron deflection in the direction of the laser pulse propagation, which favors injection into the plasma for further acceleration. We demonstrate using the OSIRIS particle-in-cell framework that the radiation reaction triggered by ultra-high laser intensity plays a crucial role in the positron injection. It provides a suppression of the initial transverse momentum gained by the positrons from the Breit-Wheeler process. For the parameters used in this work, the intensity of at least 2.2x1023 W/cm2 is needed in order to inject more than 1% of positrons created. Above this threshold, the percentage of injected positrons rapidly increases with intensity. Moreover, subsequent direct laser acceleration of positrons in a plasma channel, using the same laser pulse that created them, can ensure a boost of the final positron energy by a factor of two. The positron focusing and guiding on the axis is provided by significant electron beam loading that changes the internal structure of the channel fields.

Abstract: In this study, the first-of-its-kind use of active learning (AL) framework in thermal spray is adapted to improve the prediction accuracy of the in-flight particle characteristics and uses Gaussian Process (GP) ML model as a surrogate that generalises a global solution without necessarily involving physical mechanisms. The AL framework via the Bayesian Optimisation was utilised to: (a) reduce the maximum uncertainty in the given database and (b) reduce local uncertainty around a contrived test point. The initial dataset consists of 26 atmospheric plasma spray (APS) parameters of silicon, aimed at ceramic matrix composites (CMCs) for the next generation of aerospace applications. The maximum uncertainty in the initial dataset was reduced by AL-driven identification of search spaces and conducting six guided spray trails in the identified search spaces. On average, a 52.9% improvement (error reduction) of RMSE and an R2 increase of 8.5% were reported on the predicted in-flight particle velocities and temperatures after the AL-driven optimisation. Furthermore, the Bayesian Optimisation around a contrived test point to predict the best possible characteristics resulted in a three-fold increase in prediction accuracy as compared to the non-optimised prediction. These AL-guided experimental validations not only increase the informativeness of the limited dataset but is adaptable for other thermal spraying methods without necessarily involving physical mechanisms and underlying mechanisms. The use of AL-driven optimisation may drive the thermal spraying towards resource-efficiency and may serve as the first step towards fully digital thermal spraying environments.