Abstract
PyUnfold is a Python package for incorporating imperfections of the measurement process into a data analysis pipeline. In an ideal world, we would have access to the perfect detector: an apparatus that makes no error in measuring a desired quantity. However, in real life, detectors have finite resolutions, characteristic biases that cannot be eliminated, less than full detection efficiencies, and statistical and systematic uncertainties. By building a matrix that encodes a detector's smearing of the desired true quantity into the measured observable(s), a deconvolution can be performed that provides an estimate of the true variable. This deconvolution process is known as unfolding. The unfolding method implemented in PyUnfold accomplishes this deconvolution via an iterative procedure, providing results based on physical expectations of the desired quantity. Furthermore, tedious book-keeping for both statistical and systematic errors produces precise final uncertainty estimates.
Highlights
PyUnfold is an extensible framework for the unfolding of discrete probability distributions via the iterative unfolding method described in (D’Agostini 1995)
Long as it is possible to encode estimable resolutions and biases connecting causes to effects in a binned response matrix, one can perform a deconvolution with PyUnfold
The deconvolution packages used in high-energy physics (HEP) maintain a strong dependence on the ROOT data analysis framework (ROOT 1997), which is almost exclusively used in HEP
Summary
By building a matrix that encodes a detector’s smearing of the desired true quantity into the measured observable(s), a deconvolution can be performed that provides an estimate of the true variable. This deconvolution process is known as unfolding. PyUnfold is an extensible framework for the unfolding of discrete probability distributions via the iterative unfolding method described in (D’Agostini 1995).
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