Inductively-coupled plasma discharge for use in high-energy-density science experiments
Journal of Instrumentation IOP Publishing 18 (2023) P04008
Abstract:
Inductively-coupled plasma discharges are well-suited as plasma sources for experiments in fundamental high-energy density science, which require large volume and stable plasmas. For example, experiments studying particle beam-plasma instabilities and the emergence of coherent macroscopic structures — which are key for modelling emission from collisionless shocks present in many astrophysical phenomena. A meter-length, table-top, inductive radio-frequency discharge has been constructed for use in a high-energy density science experiment at CERN which will study plasma instabilities of a relativistic electron-positron beam. In this case, a large volume is necessary for the beam to remain inside the plasma as it diverges to centimeter-scale diameters during the tens-of-centimeters of propagation needed for instabilities to develop. Langmuir probe measurements of the plasma parameters show that plasma can be stably sustained in the discharge with electron densities exceeding 1011 cm-3. The discharge has been assembled using commercially-available components, making it an accessible option for commissioning at a University laboratory.Probing bulk electron temperature via x-ray emission in a solid density plasma
Plasma Physics and Controlled Fusion IOP Publishing 65:4 (2023) 045005
Stability of the Modulator in a Plasma-Modulated Plasma Accelerator
(2023)
Hyperspectral compressive wavefront sensing
High Power Laser Science and Engineering Cambridge University Press 11 (2023) e32
Abstract:
Presented is a novel way to combine snapshot compressive imaging and lateral shearing interferometry in order to capture the spatio-spectral phase of an ultrashort laser pulse in a single shot. A deep unrolling algorithm is utilized for snapshot compressive imaging reconstruction due to its parameter efficiency and superior speed relative to other methods, potentially allowing for online reconstruction. The algorithm’s regularization term is represented using a neural network with 3D convolutional layers to exploit the spatio-spectral correlations that exist in laser wavefronts. Compressed sensing is not typically applied to modulated signals, but we demonstrate its success here. Furthermore, we train a neural network to predict the wavefronts from a lateral shearing interferogram in terms of Zernike polynomials, which again increases the speed of our technique without sacrificing fidelity. This method is supported with simulation-based results. While applied to the example of lateral shearing interferometry, the methods presented here are generally applicable to a wide range of signals, including Shack–Hartmann-type sensors. The results may be of interest beyond the context of laser wavefront characterization, including within quantitative phase imaging.Correlation energy of the paramagnetic electron gas at the thermodynamic limit
Physical Review B American Physical Society 107 (2023) L121105