KEYWORDS: Sensors, Data modeling, Single photon, Calibration, Neural networks, Electronics, Monte Carlo methods, Picosecond phenomena, Machine learning, Performance modeling
Currently new applications for single photon imaging detectors, are challenging algorithmic signal processing approaches due to increasing photon event rates. This research explores a potential solution of machine learning (ML) algorithms for data analysis and imaging with single photon timing detectors with 16 ×16 pixels and 60 ps timing resolution. This novel ML approach will accelerate the data processing pipeline, which must process huge volumes of data, up to 10 Gbps per detector, with hundreds of detectors in certain applications. The ML model processes the photon detector output, applying spatial/temporal clustering to improve the photon detector spatial resolution with a time constraint of 10 µs.
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