Volume 414 -
41st International Conference on High Energy physics (ICHEP2022) -
Computing and Data Handling
pyhf: a pure-Python implementation of HistFactory with tensors and automatic differentiation
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Pre-published on:
November 28, 2022
Published on:
June 15, 2023
Abstract
The HistFactory p.d.f. template is per-se independent of its implementation in ROOT and it is use- ful to be able to run statistical analysis outside of the ROOT, RooFit, RooStats framework. pyhf is a pure-Python implementation of that statistical model for multi-bin histogram-based analy- sis and its interval estimation is based on the asymptotic formulas of “Asymptotic formulae for likelihood-based tests of new physics”. pyhf supports modern computational graph libraries such as TensorFlow, PyTorch, and JAX in order to make use of features such as auto-differentiation and GPU acceleration. In addition, pyhf’s JSON serialization specification for HistFactory models has been used to publish 23 full probability models from published ATLAS collaboration analyses to HEPData.
DOI: https://doi.org/10.22323/1.414.0245
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Copyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.