pyhf: Accelerating analyses and preserving likelihoods

The HistFactory p.d.f. template [CERN-OPEN-2012-016] 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 and PyTorch in order to make use of features such as autodifferentiation and GPU acceleration, resulting in multiple times speedup of analysis fits. In addition, the JSON Schema developed to support the HistFactory specification has enabled for the open publication of four full likelihoods from ATLAS analyses to HEPData within a year of the first ever published. In a tutorial the core features set of pyhf will be demonstrated in Jupyter notebooks in addition to how an analyst can use both the Python and command line APIs to quickly investigate a model and perform fits in an interactive session. The enabling features of the HistFactory JSON specification will also be explored