spefit

Optimised framework for the fitting of Single Photoelectron Spectra (SPE) in order to characterize the properties of photomultipliers which influence the measured illumination response.

• Supported Python versions: 3.10+
• Supported platforms: Linux, OSX
• Source: https://gitlab.cta-observatory.org/cta-consortium/aswg/tools/spefit
• Pypi: https://pypi.org/project/spefit
• License: BSD-3-Clause
• Citation: pending

Package Features

• Basic numpy API
• Runtime-selectable Probability Density Functions (PDFs), optimised using numba
• PDFs for the SPE spectra of both Photomultiplier Tubes and Silicon Photomultipliers
• Configuration of PDFs for the case where no pedestal peak exists (e.g. dark counting)
• Estimation of SPE parameters for improved initial fit values
• Runtime-selectable minimization cost definitions, optimised using numba
• Simultaneous fitting of multiple datasets (e.g. containing different average illuminations) for better parameter constraining
• Minimization provided by iminuit - Python frontend to the MINUIT2 C++ library
• Calculation of parameter errors and resulting p-value
• Extendable to allow the inclusion of any additional SPE description and minimization cost definitions
• Compatible with other minimization routines
• Convenience class provided for the parallel processing of cameras containing multiple photomultiplier pixels

Currently Implemented:

SPE formula:

• PMT Single Gaussian
• SiPM Gentile
• SiPM Modified Poisson

Minimization Cost functions:

• Unbinned Negative Log-likelihood
• Binned Negative Log-likelihood
• Least Squares

Installation

The package is available via pypi:

pip install spefit

Alternatively, this package is installable via pip directly from this repository (via https or ssh), e.g.:

pip install 'git+https://gitlab.cta-observatory.org/cta-consortium/aswg/tools/spefit.git#egg=spefit'

Also see the justfile for development commands.

Optional Dependencies

Utilisation of Intel's short vector math library (SVML) for improved performance provided by numba:

conda install -c numba icc_rt

Usage

With a numpy array of size (n_events) called charge_array, containing the measured charges from the low illumination of a photomultiplier, the parameters of the SPE spectra can be extracted with:

from spefit.dataset import Dataset
from spefit.pdf import PMTSingleGaussian
from spefit.cost import BinnedNLL
from spefit.fitter import minimize_with_iminuit

charges = [Dataset(charge_array, n_bins=100, range_=(-3, 6))]
pdf = PMTSingleGaussian()
cost = BinnedNLL(pdf, charges)
values, errors = minimize_with_iminuit(cost)

Jupyter notebook demonstrations are provided in tutorials.