PyHEP.dev 2025 - "Python in HEP" Developer's Workshop #48
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https://indico.cern.ch/event/1515852/
Abstract: Graph Me If You Can: Modern Python Meets HEP Statistical Models
Statistical tooling in the scientific python ecosystem continues to advance, while at the same time
ROOThas recently adopted the HEP Statistics Serialization Standard (HS3) as the way of serializing RooWorkspaces for any probability model that has been built. There is a gap between packages such asjaxandscipy.statsand whatHS3provides. This is wherepyhs3comes in—a modern Python implementation ofHS3designed with modern scientific python development practices. Prioritizing a developer-friendly interface and cross-platform compatibility,pyhs3provides a python-callable function built from the computational graph encoded in serializedHS3probability models.The goal of this effort is to facilitate existing efforts in statistical inference (pyhf, zfit, cabinetry) and auto-differentiability (neos, MadJax, evermore, relaxed) by providing a common core for bidirectional translation of
HS3-compatible workspaces.We'll discuss the design of the library, how the pieces are defined, how to extend or contribute to it, and proof-of-concept with a real-world workspace from the ATLAS$HH\to bb\gamma\gamma$ analysis. The talk presents the
pyhs3package as a step towards a common 'inference API' and providing implementations of many mathematical probability distributions common in HEP.Beta Was this translation helpful? Give feedback.
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