https://doi.org/10.1140/epjc/s10052-022-10444-4
Regular Article - Experimental Physics
Skewness-based characterization of silicon photomultipliers
P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Leninskiy prospekt 53, Moscow, Russia
Received:
3
August
2021
Accepted:
17
May
2022
Published online:
27
May
2022
Characterization of SiPMs is an objective of high importance in almost any research, development, and application of these unique photon detectors. Two decades of characterization method developments resulted in a comprehensive and elegant methodology based on a precisely resolved number of fired SiPM cells or the SiPM spectrum. Spectrum fitting procedures are very sensitive to a proper selection of many initial guess values including parameters of minor importance and happen to be unreliable. Moreover, conventional methods are useless for degraded or unresolved SiPM spectra. This challenge was anticipated for SiPM-based detectors of high-luminosity particle calorimeters where a severe radiation degradation of the SiPMs would co-exist with their acceptable performance. To address it, one can measure a mean and variance of SiPM charge responses relying on a priori known Excess Noise Factor of the SiPM but its stability during SiPM degradation is uncertain. This study proposes and evaluates a new approach for the SiPM characterization based on the first three statistical moments (mean, variance, and skewness) of its charge response including skewness. It assumes the Generalized Poisson distribution of the number of fired cells and provides simple closed-form analytical expressions for the parameter estimation. It allows determining a gain, a number of detected photons, and a probability of correlated events directly from raw data. The skewness-based characterization is anticipated to be especially useful for mass testing and continuous monitoring of SiPMs in large-scale experiments due to its simplicity and robustness. Initial evaluations of the method show promising results.
© The Author(s) 2022
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Funded by SCOAP3
