Jul 6 – 13, 2022
Bologna, Italy
Europe/Rome timezone

pyhf: a pure-Python statistical fitting library with tensors and automatic differentiation

Jul 8, 2022, 5:45 PM
Room 12 (Celeste)

Room 12 (Celeste)

Parallel Talk Computing and Data handling Computing and Data handling


Matthew Feickert (University of Illinois at Urbana-Champaign)


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" [arXiv:1007.1727]. 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 18 full probability models from published ATLAS collaboration analyses to HEPData.

In-person participation Yes

Primary authors

Giordon Stark (SCIPP, UC Santa Cruz) Matthew Feickert (University of Illinois at Urbana-Champaign) Lukas Heinrich (Technical University of Munich)

Presentation materials