Machine learning based unfolding of x-ray spectra from filter stack spectrometer data.

We demonstrate the application of neural networks to perform x-ray spectra unfolding from data collected by filter stack spectrometers. A filter stack spectrometer consists of a series of filter-detector pairs, where the detectors behind each filter measure the energy deposition through each layer as photo-stimulated luminescence (PSL). The network is trained on synthetic data, assuming x-rays of energies <1 MeV and of two different distribution functions (Maxwellian and Gaussian) and the corresponding measured PSL values obtained from five different filter stack spectrometer designs. Predicted unfolds of single distributions are near identical reproductions of the ground truth spectra, with differences in the values lower than 20% at the higher energy end in some cases. The neural network has also demonstrated robustness to experimental measurement errors of <5% and some capability of performing unfolds for linear combinations of the two distributions without previous training. The network can perform unfolds at rates >1 Hz, ideal for application to some high-repetition-rate systems.

Reduced-order model to approximate response matrices for filter stack spectrometers.

We present a reduced-order model to calculate response matrices rapidly for filter stack spectrometers (FSSs). The reduced-order model allows response matrices to be built modularly from a set of pre-computed photon and electron transport and scattering calculations through various filter and detector materials. While these modular response matrices are not appropriate for high-fidelity analysis of experimental data, they encode sufficient physics to be used as a forward model in design optimization studies of FSSs, particularly for machine learning approaches that require sampling and testing a large number of FSS designs.

Robust unfolding of MeV x-ray spectra from filter stack spectrometer data.

We present an inversion method capable of robustly unfolding MeV x-ray spectra from filter stack spectrometer (FSS) data without requiring an a priori specification of a spectral shape or arbitrary termination of the algorithm. Our inversion method is based upon the perturbative minimization (PM) algorithm, which has previously been shown to be capable of unfolding x-ray transmission data, albeit for a limited regime in which the x-ray mass attenuation coefficient of the filter material increases monotonically with x-ray energy. Our inversion method improves upon the PM algorithm through regular smoothing of the candidate spectrum and by adding stochasticity to the search. With these additions, the inversion method does not require a physics model for an initial guess, fitting, or user-selected termination of the search. Instead, the only assumption made by the inversion method is that the x-ray spectrum should be near a smooth curve. Testing with synthetic data shows that the inversion method can successfully recover the primary large-scale features of MeV x-ray spectra, including the number of x-rays in energy bins of several-MeV widths to within 10%. Fine-scale features, however, are more difficult to recover accurately. Examples of unfolding experimental FSS data obtained at the Texas Petawatt Laser Facility and the OMEGA EP laser facility are also presented.

Machine learning for detection of 3D features using sparse x-ray tomographic reconstruction.

In many inertial confinement fusion (ICF) experiments, the neutron yield and other parameters cannot be completely accounted for with one and two dimensional models. This discrepancy suggests that there are three dimensional effects that may be significant. Sources of these effects include defects in the shells and defects in shell interfaces, the fill tube of the capsule, and the joint feature in double shell targets. Due to their ability to penetrate materials, x rays are used to capture the internal structure of objects. Methods such as computational tomography use x-ray radiographs from hundreds of projections, in order to reconstruct a three dimensional model of the object. In experimental environments, such as the National Ignition Facility and Omega-60, the availability of these views is scarce, and in many cases only consists of a single line of sight. Mathematical reconstruction of a 3D object from sparse views is an ill-posed inverse problem. These types of problems are typically solved by utilizing prior information. Neural networks have been used for the task of 3D reconstruction as they are capable of encoding and leveraging this prior information. We utilize half a dozen, different convolutional neural networks to produce different 3D representations of ICF implosions from the experimental data. Deep supervision is utilized to train a neural network to produce high-resolution reconstructions. These representations are used to track 3D features of the capsules, such as the ablator, inner shell, and the joint between shell hemispheres. Machine learning, supplemented by different priors, is a promising method for 3D reconstructions in ICF and x-ray radiography, in general.

Vacuum laser acceleration of super-ponderomotive electrons using relativistic transparency injection.

Intense lasers can accelerate electrons to very high energy over a short distance. Such compact accelerators have several potential applications including fast ignition, high energy physics, and radiography. Among the various schemes of laser-based electron acceleration, vacuum laser acceleration has the merits of super-high acceleration gradient and great simplicity. Yet its realization has been difficult because injecting free electrons into the fast-oscillating laser field is not trivial. Here we demonstrate free-electron injection and subsequent vacuum laser acceleration of electrons up to 20 MeV using the relativistic transparency effect. When a high-contrast intense laser drives a thin solid foil, electrons from the dense opaque plasma are first accelerated to near-light speed by the standing laser wave in front of the solid foil and subsequently injected into the transmitted laser field as the opaque plasma becomes relativistically transparent. It is possible to further optimize the electron injection/acceleration by manipulating the laser polarization, incident angle, and temporal pulse shaping. Our result also sheds light on the fundamental relativistic transparency process, crucial for producing secondary particle and light sources.

Radiation magnetohydrodynamic simulation of plasma formed on a surface by a megagauss field.

Radiation magnetohydrodynamic modeling is used to study the plasma formed on the surface of a cylindrical metallic load, driven by megagauss magnetic field at the 1MA Zebra generator (University of Nevada, Reno). An ionized aluminum plasma is used to represent the "core-corona" behavior in which a heterogeneous Z-pinch consists of a hot low-density corona surrounding a dense low-temperature core. The radiation dynamics model included simultaneously a self-consistent treatment of both the opaque and transparent plasma regions in a corona. For the parameters of this experiment, the boundary of the opaque plasma region emits the major radiation power with Planckian black-body spectrum in the extreme ultraviolet corresponding to an equilibrium temperature of 16 eV. The radiation heat transport significantly exceeds the electron and ion kinetic heat transport in the outer layers of the opaque plasma. Electromagnetic field energy is partly radiated (13%) and partly deposited into inner corona and core regions (87%). Surface temperature estimates are sensitive to the radiation effects, but the surface motion in response to pressure and magnetic forces is not. The general results of the present investigation are applicable to the liner compression experiments at multi-MA long-pulse current accelerators such as Atlas and Shiva Star. Also the radiation magnetohydrodynamic model discussed in the paper may be useful for understanding key effects of wire array implosion dynamics.

Thin-foil fabrication.

A method has been developed to produce seamless, freestanding plastic cylinders with dimensions up to 18 cm in diameter, 4 cm in height and with mass density from 15 mug/cm to 300 mug/cm (approximately 0.15 to 3 mu thick). The polyvinyl formal resin may be subsequently coated with a variety of material by vapor deposition. The method employs standard thin-foil fabrication techniques modified for use in cylindrical geometry and results in uniform reproducible cylinders.