Sunny Howard

Abstract:

Recent advances in high-energy and high-peak-power laser systems have opened up new possibilities for fundamental physics research. In this work, the potential of twisted light for the generation of gravitational waves in the high frequency regime is explored for the first time. Focusing on Bessel beams, novel analytic expressions and numerical computations for the generated metric perturbations and associated powers are presented. The gravitational peak intensity is shown to reach 1.44 × 10−5 W.m−2 close to the source, and 1.01 × 10−19 W.m−2 ten meters away. Compelling evidence is provided that the properties of the generated gravitational waves, such as frequency, polarisation states and direction of emission, are controllable by the laser pulse parameters and optical arrangements.

Abstract:

The commissioning of multi-petawatt class laser facilities around the world is gathering pace. One of the primary motivations for these investments is the acceleration of high-quality, low-emittance electron bunches. Here we explore the interaction of a high-intensity femtosecond laser pulse with a mass-limited dense target to produce MeV attosecond electron bunches in transmission and confirm with three-dimensional simulation that such bunches have low emittance and nano-Coulomb charge. We then perform a large parameter scan from non-relativistic laser intensities to the laser-QED regime and from the critical plasma density to beyond solid density to demonstrate that the electron bunch energies and the laser pulse energy absorption into the plasma can be quantitatively described via the Zero Vector Potential mechanism. These results have wide-ranging implications for future particle accelerator science and associated technologies.

Abstract:

Low convergence ratio implosions (where wetted-foam layers are used to limit capsule convergence, achieving improved robustness to instability growth) and auxiliary heating (where electron beams are used to provide collisionless heating of a hotspot) are two promising techniques that are being explored for inertial fusion energy applications. In this paper, a new analytic study is presented to understand and predict the performance of these implosions. Firstly, conventional gain models are adapted to produce gain curves for fixed convergence ratios, which are shown to well-describe previously simulated results. Secondly, auxiliary heating is demonstrated to be well understood and interpreted through the burn-up fraction of the deuterium-tritium fuel, with the gradient of burn-up with respect to burn-averaged temperature shown to provide good qualitative predictions of the effectiveness of this technique for a given implosion. Simulations of auxiliary heating for a range of implosions are presented in support of this and demonstrate that this heating can have significant benefit for high gain implosions, being most effective when the burn-averaged temperature is between 5 and 20 keV.

Abstract:

Optical imaging systems are inherently limited in their resolution due to the point spread function (PSF), which applies a static, yet spatially-varying, convolution to the image. This degradation can be addressed via Convolutional Neural Networks (CNNs), particularly through deblurring techniques. However, current solutions face certain limitations in efficiently computing spatially-varying convolutions. In this paper we propose CoordGate, a novel lightweight module that uses a multiplicative gate and a coordinate encoding network to enable efficient computation of spatially-varying convolutions in CNNs. CoordGate allows for selective amplification or attenuation of filters based on their spatial position, effectively acting like a locally connected neural network. The effectiveness of the CoordGate solution is demonstrated within the context of U-Nets and applied to the challenging problem of image deblurring. The experimental results show that CoordGate outperforms existing approaches, offering a more robust and spatially aware solution for CNNs in various computer vision applications.

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.