pyhf: pure-Python implementation of HistFactory with tensors and automatic differentiation

Matthew Feickert; Lukas Heinrich; Giordon Stark

pyhf: pure-Python implementation of HistFactory with tensors and automatic differentiation

Matthew Feickert
, Lukas Heinrich
, Giordon Stark

Assistant Professorship of Data Science in Physics

University of Wisconsin-Madison
University of California, Santa Cruz

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

The HistFactory p.d.f. template is per-se independent of its implementation in ROOT and it is useful 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 analysis 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.

Original language	English
Article number	245
Journal	Proceedings of Science
Volume	414
State	Published - 2022
Event	41st International Conference on High Energy Physics, ICHEP 2022 - Bologna, Italy Duration: 6 Jul 2022 → 13 Jul 2022

Cite this

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title = "pyhf: pure-Python implementation of HistFactory with tensors and automatic differentiation",

abstract = "The HistFactory p.d.f. template is per-se independent of its implementation in ROOT and it is useful 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 analysis and its interval estimation is based on the asymptotic formulas of {\textquotedblleft}Asymptotic formulae for likelihood-based tests of new physics{\textquotedblright}. 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.",

author = "Matthew Feickert and Lukas Heinrich and Giordon Stark",

note = "Publisher Copyright: {\textcopyright} Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0); 41st International Conference on High Energy Physics, ICHEP 2022 ; Conference date: 06-07-2022 Through 13-07-2022",

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T2 - 41st International Conference on High Energy Physics, ICHEP 2022

AU - Feickert, Matthew

AU - Heinrich, Lukas

AU - Stark, Giordon

N1 - Publisher Copyright: © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

PY - 2022

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N2 - The HistFactory p.d.f. template is per-se independent of its implementation in ROOT and it is useful 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 analysis 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.

AB - The HistFactory p.d.f. template is per-se independent of its implementation in ROOT and it is useful 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 analysis 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.

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M3 - Conference article

AN - SCOPUS:85149937629

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JO - Proceedings of Science

JF - Proceedings of Science

M1 - 245

Y2 - 6 July 2022 through 13 July 2022

ER -

pyhf: pure-Python implementation of HistFactory with tensors and automatic differentiation

Abstract

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