Simulation-Based Inference Blueprint

Session

SBI with semi-parameterized Density Ratios

27 Feb 2026, 13:30

13/2-005 (CERN)

28. Building summaries with event2vec

Nick Smith (Fermi National Accelerator Lab. (US)), Prasanth Shyamsundar (Fermi National Accelerator Laboratory)

27/02/2026, 13:30

In this work, we introduce some machine learning techniques for training sensitive vector-representations or vector-summaries of collider events. The vector-summaries of the individual events in a dataset can be directly summed up and analyzed further. For EFT searches, our approach leads to a powerful and convenient middle ground between traditional histogram-based analyses and SBI analyses...

29. Parametrized Optimal Observable Approach to NSBI

Matthew Kenneth Maroun (University of Massachusetts (US))

27/02/2026, 13:50

The parametrized optimal observable approach is a binned approximation to the full NSBI formalism introduced in [Rep. Prog. Phys. 88 (2025) 067801]. We present the method highlighting it's advantages and limitations. We will show a practical implementation of the parametrized optional observable formalism in RooFit and show how it can be used to construct Asimov datasets and to perform Neyman...

22. Tooling issues for binned nSBI analyses with pyhf and how to overcome them

Malin Elisabeth Horstmann (Technische Universitat Munchen (DE))

27/02/2026, 14:10

Neural estimates of likelihood ratios provide a powerful approach to extending sensitivity across wide regions of phase space, but their integration into full HEP analyses presents significant technical challenges. The computational cost of unbinned neural simulation-based inference (nSBI) can be reduced by performing binned fits using optimal observables - whilst still retaining the benefits...

30. Discussion

27/02/2026, 14:30