CoordGate: efficiently computing spatially-varying convolutions in convolutional neural networks

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

Hyperspectral compressive wavefront sensing

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

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 non-linear nature of the process. Here, a novel method to retrieve quantitative information from shadowgrams, based on computational geometry, is presented for the first time. This process can 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 post-processing techniques. This adds a powerful new tool for research in various fields in engineering and physics for both techniques

Nonlinear wakefields and electron injection in cluster plasma

Laser and beam driven wakefields promise orders of magnitude increases in electric field gradients for particle accelerators for future applications. Key areas to explore include the emittance properties of the generated beams and overcoming the dephasing limit in the plasma. In this paper, the first in-depth study of the self-injection mechanism into wakefield structures from non-homogeneous cluster plasmas is provided using high-resolution two dimensional particle-in-cell simulations. The clusters which are typical structures caused by ejection of gases from a high-pressure gas jet have a diameter much smaller than the laser wavelength. Conclusive evidence is provided for the underlying mechanism that leads to particle trapping, comparing uniform and cluster plasma cases. The accelerated electron beam properties are found to be tunable by changing the cluster parameters. The mechanism explains enhanced beam charge paired with large transverse momentum and energy which has implications for the betatron x-ray flux. Finally, the impact of clusters on the high-power laser propagation behavior is discussed

Hyperspectral Compressive Wavefront Sensing

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 utilised for the snapshot compressive imaging reconstruction due to its parameter efficiency and superior speed relative to other methods, potentially allowing for online reconstruction. The algorithm’s regularisation term is represented using 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

Simulation of density measurements in plasma wakefields using photon acceleration

One obstacle in plasma accelerator development is the limitation of techniques to diagnose and measure plasma wakefield parameters. In this paper, we present a novel concept for the density measurement of a plasma wakefield using photon acceleration, supported by extensive particle in cell simulations of a laser pulse that copropagates with a wakefield. The technique can provide the perturbed electron density profile in the laser's reference frame, averaged over the propagation length, to be accurate within 10%. We discuss the limitations that affect the measurement: small frequency changes, photon trapping, laser displacement, stimulated Raman scattering, and laser beam divergence. By considering these processes, one can determine the optimal parameters of the laser pulse and its propagation length. This new technique allows a characterization of the density perturbation within a plasma wakefield accelerator

Quantitative single shot and spatially resolved plasma wakefield diagnostics

Diagnosing plasma conditions can give great advantages in optimizing plasma wakefield accelerator experiments. One possible method is that of photon acceleration. By propagating a laser probe pulse through a plasma wakefield and extracting the imposed frequency modulation, one can obtain an image of the density modulation of the wakefield. In order to diagnose the wakefield parameters at a chosen point in the plasma, the probe pulse crosses the plasma at oblique angles relative to the wakefield. In this paper, mathematical expressions relating the frequency modulation of the laser pulse and the wakefield density profile of the plasma for oblique crossing angles are derived. Multidimensional particle-in-cell simulation results presented in this paper confirm that the frequency modulation profiles and the density modulation profiles agree to within 10%. Limitations to the accuracy of the measurement are discussed in this paper. This technique opens new possibilities to quantitatively diagnose the plasma wakefield density at known positions within the plasma column