Jon Murphy | Searcharxiv

arXiv2024

Orbital angular momentum enhanced laser absorption and neutron generation

We experimentally demonstrate enhanced absorption of near relativistic optical vortex beams in $\mathrm{D_2O}$ plasmas to generate a record fast-neutron yield of $1.45 \times 10^6$ n/s/sr. Beams with a topological charge of 5 were shown to deliver up to a 3.3 times enhancement of fast-neutron yield over a Gaussian focused beam of the same energy but having two orders of magnitude higher intensity. This result was achieved with laser energies of 16 mJ and a pulse duration of 67 fs. The Orbital Angular Momentum (OAM) beam-target interactions in our experiment were also investigated through Particle-in-Cell (PIC) simulations. Electron density rippling resulting in enhanced plasma wave excitation on the critical surface and significantly enhanced resonance absorption is observed.

physics.plasm-ph↗

arXiv2020

Beyond optimization -- supervised learning applications in relativistic laser-plasma experiments

We explore the applications of machine learning techniques in relativistic laser-plasma experiments beyond optimization purposes. We predict the beam charge of electrons produced in a laser wakefield accelerator given the laser wavefront change caused by a deformable mirror. Machine learning enables feature analysis beyond merely searching for an optimal beam charge, showing that specific aberrations in the laser wavefront are favored in generating higher beam charges. Supervised learning models allow characterizing the measured data quality as well as recognizing irreproducible data and potential outliers. We also include virtual measurement errors in the experimental data to examine the model robustness under these conditions. This work demonstrates how machine learning methods can benefit data analysis and physics interpretation in a highly nonlinear problem of relativistic laser-plasma interaction.

physics.plasm-ph↗