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Pulse timing is an important topic in nuclear instrumentation, with
far-reaching applications from high energy physics to radiation imaging.
While high-speed analog-to-digital converters become more and more
developed and accessible, their potential uses and merits in nuclear
detector signal processing are still uncertain, partially due to
associated timing algorithms which are not fully understood and utilized.
In the paper "Label-free timing analysis of SiPM-based modularized
detectors with physics-constrained deep learning", we propose a novel
method based on deep learning for timing analysis of modularized detectors
without explicit needs of labelling event data. By taking advantage of the
intrinsic time correlations, a label-free loss function with a specially
designed regularizer is formed to supervise the training of neural
networks towards a meaningful and accurate mapping function. We
mathematically demonstrate the existence of the optimal function desired
by the method, and give a systematic algorithm for training and
calibration of the model. The proposed method is validated on two
experimental datasets based on silicon photomultipliers (SiPM) as main
transducers: In the toy experiment, we collect data from a pair of SiPM
sensors from a common laser source. The neural network model achieves the
single-channel time resolution of 8.8 ps and exhibits robustness against
concept drift in the dataset. In the electromagnetic calorimeter
experiment, we collect data from an eight-channel calorimeter module.
Several neural network models (Fully-Connected, Convolutional Neural
Network and Long Short Term Memory) are tested to show their conformance
to the underlying physical constraint and to judge their performance
against traditional methods. In total, the proposed method works well in
either ideal or noisy experimental condition and recovers the time
information from waveform samples successfully and precisely. The dataset
in this repository serves as a basis for similar researches on timing
performance of SiPM-based nuclear detectors, and on application of neural
networks to typical signals of nuclear radiation detectors.
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