Machine Learning for Charged Particle Imaging Applications

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Charged particle imaging for molecular dynamics studies has significantly revolutionised experiments in gas-phase photochemistry and molecular scattering since its initial demonstration in 1987. Many improvements to this technique have been developed since its emergence, the most popular of which is known as velocity map imaging (VMI). These experiments use laser pulses to probe chemical processes (i.e. photodissociation) and ionise the photofragments. This creates a recoiling distribution which is then projected onto a position sensitive detector by use of an electrostatic lens assembly. The resulting image is then captured by a camera, providing an incredibly useful source of multiplexed energy- and angle-resolved data that can yield a great deal of insight into the photochemical dynamics of gas-phase molecules. A plethora of post-processing techniques can be implemented on these images for numerous applications, and many of these transformations (such as the removal of noise, or super-resolution imaging) can be significantly enhanced using machine learning techniques: In contrast to traditional computing, where input data is transformed via a predetermined algorithm to produce a specific output, machine learning switches up this process by using many pairs of known input/output combinations to instead develop the actual transformation algorithm. Once this "training" phase is complete, additional input images may be processed as required. This allows us to tackle problems that would otherwise be very challenging - particularly where analytical mathematical solutions are not possible. Previous VMI applications exploiting machine learning include the removal of noise from experimental data [ChemPhysChem, 22, 76, (2021)] and the ability to "reinflate" the original 3D spherical distribution of ions/electrons from just a single 2D projection [Rev. Sci. Instrum., 93, 023303, (2022)]. The proposed PhD project will build upon this work and develop additional neural networks to tackle a much wider range of problems relevant to charged particle image processing. This includes, for example, image up-sampling for next generation imaging detectors, novel strategies for polarization diagnostics in the vacuum ultraviolet spectral region, and denoising data sparse images in the temporal domain. This will involve extensive computational/numerical modelling work for the accurate simulation of relevant VMI training data, as well as implementing a variety of neural network architectures. Additionally, the project will include some acquisition of experimental VMI test data to help validate the networks under development.