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.

We report the genetic algorithm-driven wavefront optimization of ultrafast Laguerre-Gaussian beams. Wavefront manipulation was performed using a deformable mirror. The results show that the intensity fluctuations along the perimeter of t he target ring-shaped profile can be reduced up to ~15%.

Structured intense laser beams offer degrees of freedom that are highly attractive for high-field science applications. However, the performance of high-power laser beams in these applications is often hindered by deviations from the desired spatiotemporal profile. This study reports the wavefront optimization of ultrafast Laguerre-Gaussian beams through the synergy of adaptive optics and genetic algorithm-guided feedback. The results indicate that the intensity fluctuations along the perimeter of the target ring-shaped profile can be reduced up to ∼15%. Furthermore, the radius of the ring beam profile can be tailored to a certain extent by establishing threshold fitting criteria. The versatility of this approach is experimentally demonstrated in conjunction with different focusing geometries.

The applications of machine learning in today’s world encompass all fields of life and physical sciences. In this paper, we implement a machine learning based algorithm in the context of laser physics and particle accelerators. Specifically, a neural network-based optimisation algorithm has been developed that offers enhanced control over an ultrafast femtosecond laser in comparison to the traditional Proportional Integral and derivative (PID) controls. This research opens a new potential of utilising machine learning and even deep learning techniques to improve the performance of several different lasers and accelerators systems.

We explore the applications of a variety of machine learning techniques in relativistic laser-plasma experiments beyond optimization purposes. With the trained supervised learning models, the beam charge of electrons produced in a laser wakefield accelerator is predicted given the laser wavefront change caused by a deformable mirror. Feature importance analysis using the trained models shows that specific aberrations in the laser wavefront are favored in generating higher beam charges, which reveals more information than the genetic algorithms and the statistical correlation do. The predictive models enable operations beyond merely searching for an optimal beam charge. The quality of the measured data is characterized, and anomaly detection is demonstrated. The model robustness against measurement errors is examined by applying a range of virtual measurement error bars to the experimental data. This work demonstrates a route to machine learning applications in a highly nonlinear problem of relativistic laser-plasma interaction for in-depth data analysis to assist physics interpretation.

We analyze the experimental data from high-intensity laser-plasma interactions using supervised learning techniques. We predict the beam charge of electrons produced in a laser wakefield accelerator given the laser wavefront change. This study shows that generating higher beam charges favors specific wavefronts, which is revealed by ranking the feature importance. These machine learning methods can help understand the measured data quality as well as recognize irreproducible data and outliers. To study science with error bars, we also include virtual measurement errors in the dataset to examine model robustness. This work demonstrates how machine learning methods can benefit data analysis and physics interpretation in a highly nonlinear problem of laser-plasma interaction.

Submitted for the DPP19 Meeting of The American Physical Society Optimization of high repetition-rate laser wakefield accelerators using machine-learning techniques1 JON MURPHY, YONG MA, MILOS BURGER, JOHN NEES, ALEC THOMAS, KARL KRUSHELNICK, University of Michigan — Many potential applications of laser accelerator sources require operation at high repetition rate. Here, 20 milliJoule pulses are generated at kilohertz repetition rate for pulse self-compression and laser wakefield acceleration experiments. A genetic algorithm is implemented using a Dazzler acousto-optic programmable dispersive filter with the laser pulse characteristics from FROG measurements or wakefield electron beam signal optimized onto several different masks used as feedback. This procedure allows a heuristic search for the optimal laser pulse phase characteristics up to 4th order to produce a desired arbitrary wakefield electron beam or a well self-compressed pulse. Additionally, in progress is the implementation of a spiral phase plate in order to produce a Laguerre−Gaussian01 laser pulse with optical angular momentum. Were investigating the use of this exotic beam for laser wakefield acceleration experiments. 1Supported by: Department of Energy/HEP DE-SC0016804 Jon Murphy University of Michigan Date submitted: 03 Jul 2019 Electronic form version 1.4

Using the spectra of 22,923 high-redshift quasars from the Baryon Oscillation Spectrosocpic Survey (BOSS) subset of the Sloan Digital Sky Survey (SDSS), the authors detect evidence of the primordial baryonic acoustic oscillations (BAOs) in the matter power spectrum. The detection further endorses the currently accepted Λ-CDM model of cosmology based upon the existence of dark energy (Λ) and cold dark matter (CDM). Additionally, the use of the continuous wavelet transform to calculate the power spectrum has many advantages over traditional Fourier methods and independently corroborates previous detections.