Oxford Centre for High Energy Density Science (OxCHEDS)

Abstract:

Modeling many-body quantum systems with strong interactions is one of the core challenges of modern physics. A range of methods has been developed to approach this task, each with its own idiosyncrasies, approximations, and realm of applicability. Perhaps the most successful and ubiquitous of these approaches is density functional theory (DFT). Its Kohn-Sham formulation has been the basis for many fundamental physical insights, and it has been successfully applied to fields as diverse as quantum chemistry, condensed matter and dense plasmas. Despite the progress made by DFT and related schemes, however, there remain many problems that are intractable for existing methods. In particular, many approaches face a huge computational barrier when modeling large numbers of coupled electrons and ions at finite temperature. Here, we address this shortfall with a new approach to modeling many-body quantum systems. Based on the Bohmian trajectories formalism, our new method treats the full particle dynamics with a considerable increase in computational speed. As a result, we are able to perform large-scale simulations of coupled electron-ion systems without employing the adiabatic Born-Oppenheimer approximation.

Abstract:

Hydrodynamic optically-field-ionized (HOFI) plasma channels up to 100mm long are investigated. Optical guiding is demonstrated of laser pulses with a peak input intensity of $6\times10^{17}$ W cm$^{-2}$ through 100mm long plasma channels with on-axis densities measured interferometrically to be as low as $n_{e0} = (1.0\pm0.3)\times10^{17}$cm$^{-3}$. Guiding is also observed at lower axial densities, which are inferred from magneto-hydrodynamic simulations to be approximately $7\times10^{16}$cm$^{-3}$. Measurements of the power attenuation lengths of the channels are shown to be in good agreement with those calculated from the measured transverse electron density profiles. To our knowledge, the plasma channels investigated in this work are the longest, and have the lowest on-axis density, of any free-standing waveguide demonstrated to guide laser pulses with intensities above $>10^{17}$ W cm$^{-2}$.

Abstract:

Our understanding of physical systems generally depends on our ability to match complex computational modelling with measured experimental outcomes. However, simulations with large parameter spaces suffer from inverse problem instabilities, where similar simulated outputs can map back to very different sets of input parameters. While of fundamental importance, such instabilities are seldom resolved due to the intractably large number of simulations required to comprehensively explore parameter space. Here we show how Bayesian machine learning can be used to address inverse problem instabilities, and apply it to two popular experimental diagnostics in plasma physics. We find that the extraction of information from measurements simply on the basis of agreement with simulations is unreliable, and leads to a significant underestimation of uncertainties. We describe how to statistically quantify the effect of unstable inverse models, and describe an approach to experimental design that mitigates its impact.