Speaker
Malin Elisabeth Horstmann
(Technische Universitat Munchen (DE))
Description
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 from a parameterised observable. However, even in for this approach, commonly used statistical tools such as QuickStats and pyhf introduce practical limitations. In this work, we identify the key technical hurdles encountered in binned nSBI analyses and demonstrate solutions that enable robust and fast-turnaround statistical inference.