James Sadler

Compression of X-ray free electron laser pulses to attosecond duration

State of the art X-ray Free Electron Laser facilities currently provide the brightest X-ray pulses available, typically with mJ energy and several hundred femtosecond duration. Here we present one- and two-dimensional Particle-in-Cell simulations, utilising the process of stimulated Raman amplification, showing that these pulses are compressed to a temporally coherent, sub-femtosecond pulse at 8% efficiency. Pulses of this type may pave the way for routine time resolution of electrons in nm size potentials. Furthermore, evidence is presented that significant Landau damping and wave-breaking may be beneficial in distorting the rear of the interaction and further reducing the final pulse duration.

Quantitative shadowgraphy and proton radiography for large intensity modulations

Shadowgraphy is a technique widely used to diagnose objects or systems in various fields in physics and engineering. In shadowgraphy, an optical beam is deflected by the object and then the intensity modulation is captured on a screen placed some distance away. However, retrieving quantitative information from the shadowgrams themselves is a challenging task because of the nonlinear nature of the process. Here, we present a method to retrieve quantitative information from shadowgrams, based on computational geometry. This process can also be applied to proton radiography for electric and magnetic field diagnosis in high-energy-density plasmas and has been benchmarked using a toroidal magnetic field as the object, among others. It is shown that the method can accurately retrieve quantitative parameters with error bars less than 10%, even when caustics are present. The method is also shown to be robust enough to process real experimental results with simple pre- and postprocessing techniques. This adds a powerful tool for research in various fields in engineering and physics for both techniques.

Optimization of plasma amplifiers

Plasma amplifiers offer a route to side-step limitations on chirped pulse amplification and generate laser pulses at the power frontier. They compress long pulses by transferring energy to a shorter pulse via the Raman or Brillouin instabilities. We present an extensive kinetic numerical study of the three-dimensional parameter space for the Raman case. Further particle-in-cell simulations find the optimal seed pulse parameters for experimentally relevant constraints. The high-efficiency self-similar behavior is observed only for seeds shorter than the linear Raman growth time. A test case similar to an upcoming experiment at the Laboratory for Laser Energetics is found to maintain good transverse coherence and high-energy efficiency. Effective compression of a 10kJ, nanosecond-long driver pulse is also demonstrated in a 15-cm-long amplifier.

Dense plasma heating by crossing relativistic electron beams

Here we investigate, using relativistic fluid theory and Vlasov-Maxwell simulations, the local heating of a dense plasma by two crossing electron beams. Heating occurs as an instability of the electron beams drives Langmuir waves, which couple nonlinearly into damped ion-acoustic waves. Simulations show a factor 2.8 increase in electron kinetic energy with a coupling efficiency of 18%. Our results support applications to the production of warm dense matter and as a driver for inertial fusion plasmas.

Machine learning applied to proton radiography of high-energy-density plasmas

Proton radiography is a technique extensively used to resolve magnetic field structures in high-energy-density plasmas, revealing a whole variety of interesting phenomena such as magnetic reconnection and collisionless shocks found in astrophysical systems. Existing methods of analyzing proton radiographs give mostly qualitative results or specific quantitative parameters, such as magnetic field strength, and recent work showed that the line-integrated transverse magnetic field can be reconstructed in specific regimes where many simplifying assumptions were needed. Using artificial neural networks, we demonstrate for the first time 3D reconstruction of magnetic fields in the nonlinear regime, an improvement over existing methods, which reconstruct only in 2D and in the linear regime. A proof of concept is presented here, with mean reconstruction errors of less than 5% even after introducing noise. We demonstrate that over the long term, this approach is more computationally efficient compared to other techniques. We also highlight the need for proton tomography because (i) certain field structures cannot be reconstructed from a single radiograph and (ii) errors can be further reduced when reconstruction is performed on radiographs generated by proton beams fired in different directions.