This study focuses on exploring the potential of Charge Division Imaging Readout (CDIR) for micro-Channel plate (MCP) based resistive sea photon detectors. The CDIR technique spreads the MCP charge footprint capacitively between readout nodes forming anode segments. Charge measurements at each node are then used to reconstruct incident photon’s position and time. A primary objective is to investigate the minimum number of anode segmentation’s necessary, to allow successful reconstruction of multiple photons within a given time interval where pile up would be an issue for traditional approaches. Allowing for optimisation of the anode structure, investigating for a readout schematic to improve timing, rate capability, and reduce distortion effects.Algorithmic and machine learning (ML) techniques will be compared and utilised to reconstruct spatial positions of multiple photons. The comparison will aim to determine if machine learning techniques can be utilised to correct for algorithmic systematic errors to provide a more robust system, whilst removing the need for complex calibrations and allowing for efficient implementation on FPGA in future work.
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