163 citations · 663 across the 18 of their papers we have counts for
4 papers · 1 filter
The MadNIS Reloaded
Theo Heimel, Nathan Huetsch, Fabio Maltoni +3
In pursuit of precise and fast theory predictions for the LHC, we present an implementation of the MadNIS method in the MadGraph event generator. A series of improvements in MadNIS…
Precision-Machine Learning for the Matrix Element Method
Theo Heimel, Nathan Huetsch, Ramon Winterhalder +2
The matrix element method is the LHC inference method of choice for limited statistics. We present a dedicated machine learning framework, based on efficient phase-space integratio…
Returning CP-Observables to The Frames They Belong
Jona Ackerschott, Rahool Kumar Barman, Dorival Gonçalves +2
Optimal kinematic observables are often defined in specific frames and then approximated at the reconstruction level. We show how multi-dimensional unfolding methods allow us to re…
How to Understand Limitations of Generative Networks
Ranit Das, Luigi Favaro, Theo Heimel +3
Well-trained classifiers and their complete weight distributions provide us with a well-motivated and practicable method to test generative networks in particle physics. We illustr…