163 citations · 663 across the 18 of their papers we have counts for
3 papers · 1 filter
Profile Likelihoods on ML-Steroids
Theo Heimel, Tilman Plehn, Nikita Schmal
Profile likelihoods, for instance, describing global SMEFT analyses at the LHC are numerically expensive to construct and evaluate. Especially profiled likelihoods are notoriously…
Differentiable MadNIS-Lite
Theo Heimel, Olivier Mattelaer, Tilman Plehn +1
Differentiable programming opens exciting new avenues in particle physics, also affecting future event generators. These new techniques boost the performance of current and planned…
The Landscape of Unfolding with Machine Learning
Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov +9
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new…