PyHEP 2025 - "Python in HEP" Users Workshop (hybrid), CERN

Session

Plenary Session Tuesday (4)

28 Oct 2025, 16:30

222/R-001 (CERN)

Conveners

Plenary Session Tuesday (4)

• Iason Krommydas (Rice University (US))

8. Smart Gradient-Based Optimization of HEP Analyses

Lino Oscar Gerlach (Princeton University (US)), Mohamed Aly (Princeton University (US))

28/10/2025, 16:30

"Long talk"

Automatic differentiation, the technique behind modern deep learning, can be applied more broadly in High Energy Physics (HEP) to make entire analysis pipelines differentiable. This enables direct optimization of analysis choices such as selection thresholds, binning strategies, and systematic treatments by propagating gradients through the statistical analysis chain.

This talk will...

23. evermore: differentiable binned likelihoods in JAX

Felix Philipp Zinn (Rheinisch Westfaelische Tech. Hoch. (DE)), Manfred Peter Fackeldey (Princeton University (US))

28/10/2025, 17:20

"Standard talk"

evermore is a software package for statistical inference using likelihood
functions of binned data. It fulfils three key concepts: performance,
differentiability, and object-oriented statistical model building.
evermore is build on JAX - a powerful autodifferentiation Python frame-
work. By making every component in evermore a “PyTree”, each compo-
nent can be jit-compiled (jax.jit),...

1. Efficient binned profile likelihood maximization with Rabbit

David Walter (Massachusetts Inst. of Technology (US))

28/10/2025, 17:40

The High-Luminosity LHC era will deliver unprecedented data volumes, enabling measurements on fine-grained multidimensional histograms containing millions of bins with thousands of events each. Achieving ultimate precision requires modeling thousands of systematic uncertainty sources, creating computational challenges for likelihood maximization and inference. Fast optimization is crucial for...