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Multi-parameter digital waveform feature extraction method for event position localization
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DOI：10.1016/j.nima.2026.171294.png)
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Abstract
En 中文
When the detection area increases and the number of SiPMs grows, accurate event localization becomes increasingly critical. This paper proposes a new event localization method for large-area silicon photomultiplier (SiPM) array readout, overcoming the limitations of conventional pulse localization approaches, which rely on amplitude ratios between pulses. Due to the resistor network, signals from SiPMs at different locations undergo distortions. We proposed a simplified model, a tri-exponential model to explain these distortions and leveraged it to design a pulse-shape discrimination (PSD) algorithm that extracts 12 position-dependent features from each signal. These features are then classified using a machine learning approach to generate a model capable of accurately determining the interaction position. The new method achieves over 95% accuracy in identifying interaction positions within an 8 x 8 detector array.
Keywords:
Resistor network
Silicon photomultipliers (SiPMs)
Pulse shape discrimination (PSD)
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