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A simultaneous high-resolution x-ray backlighting and self-emission imaging method for laser-produced plasma diagnostics is developed in which two Kirkpatrick–Baez imaging channels for high-energy and low-energy diagnostics are constructed using a combination of multilayer mirrors in near-coaxial form. By using a streak or framing camera placed on the image plane, both backlit and self-emission images of a laser-produced plasma with high spatial and temporal resolution can be obtained simultaneously in a single shot. This paper describes the details of the method with regard to its optical and multilayer design, assembly, and alignment method. In addition, x-ray imaging results with a spatial resolution better than 5 µm in the laboratory and experimental results with imploding capsules in the SG-III prototype laser facility are presented.

Although the streaked optical pyrometer (SOP) system has been widely adopted in shock temperature measurements, its reliability has always been of concern. Here, two calibrated Planckian radiators with different color temperatures were used to calibrate and verify the SOP system by comparing the two calibration standards using both multi-channel and single-channel methods. A high-color-temperature standard lamp and a multi-channel filter were specifically designed for the measurement system. To verify the reliability of the SOP system, the relative deviation between the measured data and the standard value of less than 5% was calibrated out, which demonstrates the reliability of the SOP system. Furthermore, a method to analyze the uncertainty and sensitivity of the SOP system is proposed. A series of laser-induced shock experiments were conducted at the ‘Shenguang-II’ laser facility to verify the reliability of the SOP system for temperature measurements at tens of thousands of kelvin. The measured temperature of the quartz in our experiments agreed fairly well with previous works, which serves as evidence for the reliability of the SOP system.

Temperature is one of the most important parameters for characterizing the thermodynamic state of matter in extreme conditions. However, there is as of yet no universal and accurate way to measure the temperature associated with a shock wave propagating in an opaque material, let alone an inversion method for determining how this temperature evolves. Based on the current strong generalization and learning abilities of artificial neural networks, this paper proposes using an artificial neural network to determine (i) how the shock-wave temperature in a material evolves and (ii) the surface temperature of the interface between the material and vacuum when a shock wave propagates through the material. Data generated using a one-dimensional numerical hydrodynamic simulation are used to train the artificial neural network by applying backpropagation and optimization to many datasets. Once the artificial neural network is trained sufficiently, it becomes an excellent approximator that can estimate the shock-wave temperature from a given streaked-optical-pyrometer image and other known information from the experiment. The paper ends with various possible extensions to the present research.

An aberration-free imaging technique was used to design a double-spherically bent crystal spectrometer with high energy and spatial resolutions to ensure that the individual spectral lines are represented as perfectly straight lines on the detector. After obtaining the matched parameters of the two crystals via geometry-based optimization, an alignment method was employed to allow the spacing between the crystals and the detector to be coupled with the source. The working principle of this spectrum-measuring scheme was evaluated using a Cu X-ray tube. High-quality spectra with energy resolutions (E/ΔE) of approximately 3577 were obtained for a relatively large source size.