PeakOTron: A Python Module for Fitting Charge Spectra of Silicon Photomultipliers

TL;DR

PeakOTron is a Python tool that accurately fits charge spectra of silicon photomultipliers, enabling detailed characterization of device parameters from experimental data and simulations.

Contribution

It introduces a comprehensive Python module for fitting SiPM charge spectra, extracting multiple parameters simultaneously with high precision, and is suitable for large-scale automated analyses.

Findings

Parameters are precisely determined with biases and uncertainties quantified.

The model fits well with a $ ext{chi}^2$/NDF close to 1 for simulated data.

Experimental spectra analysis shows good agreement with manufacturer specifications.

Abstract

A Python program has been developed which fits a published detector-response model to SiPM charge spectra to characterise SiPMs. Spectra for SiPMs illuminated by low intensity pulsed light with Poisson-distributed number of photons and a time spread of order nanoseconds or less, can be analysed. The entire charge spectra, including the intervals in-between the photoelectron peaks, are fitted, which allows determining, in addition to the mean number of detected photons, gain, gain spread, prompt cross-talk, pedestal, and electronics noise, the dark-count rate as well as the probability and time constant of after-pulses. The starting values of the fit parameters are extracted from the charge spectra. The program performance has been evaluated using simulated charge spectra with the different SiPM parameters varied in a wide range. By analysing 100 simulated spectra for every parameter set, the biases and statistical uncertainties of the individual parameters have been determined. It is found that the parameters are precisely determined and that the entire spectra are well described, in most cases with a $\chi ^2$/NDF close to 1. In addition, measured spectra for two types of SiPMs for a wide range of over-voltages have been analysed. The program achieves mostly a good description of the spectra, and the parameters determined agree with the values from the producers and expectations. The program can be used for detailed analyses of single spectra, but, as it is compatible with the native \texttt{Python} multiprocessing module, also for the automatic characterisation of large samples of SiPMs.