pyhf: a pure-Python implementation of HistFactory with tensors and automatic differentiation
M. Feickert, Lukas Heinrich, Giordon Holtsberg Stark · Proceedings of 41st International Conference on High Energy physics — PoS(ICHEP2022) · 2022
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.