Accurately determining the speed of a shock front, moving at several kilometres per second, is an important measurement in high-energy-density HED physics experiments. In this paper, we have developed a novel approach to analysing shock-speed data by leveraging the latest techniques in probabilistic machine learning (ML). Laser light is reflected off the moving shock and combined with other, non-reflected, light to produce an oscillating signal. Conventionally, this signal is then split into its component wavelengths, based on several choices made by the scientist, and used to work out the shock speed. However, by using our probabilistic ML approach we are able to avoid this step entirely and determine the speed, with uncertainty, directly from the original data. This provides an automated and robust approach that can be used even for very noisy or missing experimental data.
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This article appears in Rev. Scien. Instruments: Vol. 96, issue 8, August 2025 – https://doi.org/10.1063/5.0267409