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.

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.

A modular, high dynamic range passive neutron dosimeter and imaging diagnostic.

The multi-decade neutron dosimeter and imaging diagnostic (MDND) is a passive diagnostic that utilizes the polyethylene (n, p) nuclear reaction to enhance the diagnostic's sensitivity for time and energy integrated neutron measurements in the range of 2.45-14.1 MeV. The MDND utilizes a combination of radiochromic film, phosphor image plates, and solid-state nuclear track detectors, with the goal of providing several orders of magnitude of dynamic range in terms of measured neutron fluence. The diagnostic design was guided by simulations in the Monte Carlo N-Particle (MCNP) transport code to determine the optimum thickness of the polyethylene convertor for maximum proton fluence incident on the detection medium as a function of incident neutron energy. In addition, the simulation results of complete diagnostic assemblies, or "stacks," were used to determine the total dynamic range of an MDND in terms of measured neutron source yield, which was found to be between around 107 and 1015 emitted into 4π with the detector located 1 m away from the source. Complimentary to these simulations, individual detectors within a stack were simulated and analyzed to determine response as a function of neutron energy and yield. This work presents the diagnostic design, MCNP simulation results, and analysis of expected signals for varying neutron sources.

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.