Indico has been updated to v3.3. See our blog post for details on this release. (OTG0146394)

Mar 10 – 15, 2019
Steinmatte conference center
Europe/Zurich timezone

pyhf: auto-differentiable binned statistical models

Not scheduled
Steinmatte conference center

Steinmatte conference center

Hotel Allalin, Saas Fee, Switzerland
Poster Track 2: Data Analysis - Algorithms and Tools Poster Session


Lukas Alexander Heinrich (New York University (US))


A large class of statistical models in high energy physics can be expressed a simultaneous measurement of binned observables. A popular framework for such binned analysis is HistFactory. So far the only implementation of the model has been within the ROOT ecosystem, limiting adoption and extensibility. We present a complete and extensible implementation of the HistFactory class of models in python, based on multi-dimensional multiarray (tensor) computations. The implementation allows for a variety of tensor backends, such as numpy, TensorFlow, PyTorch and Dask. Through the latter, likelihoods are expressed as computational graphs suitable for automatic differentiation and hardware-accelerated (e.g. GPU and TPU-based) or distributed computation. We present benchmarks showing significant performance gains compared to the existing implementation. Further the implementation introduces a new serialization format for binned likelihoods suitable for archiving on community repositories such as HepData, providing an attractive interface for reinterpretation of physics analyses.

Primary authors

Lukas Alexander Heinrich (New York University (US)) Kyle Stuart Cranmer (New York University (US)) Matthew Feickert (Southern Methodist University (US)) Giordon Holtsberg Stark (University of California,Santa Cruz (US))

Presentation materials