Modern machine learning is transforming particle physics fast, bullying its way into our
numerical tool box. For young researchers it is crucial to stay on top of this development …

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 …

In this work we introduce ν 2-flows, an extension of the ν-flows method to final states
containing multiple neutrinos. The architecture can natively scale for all combinations of …

We review the main applications of machine learning models that are not fully supervised in
particle physics, ie, clustering, anomaly detection, detector simulation, and unfolding …

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 …

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 …

We introduce two diffusion models and an autoregressive transformer for LHC physics
simulations. Bayesian versions allow us to control the networks and capture training …

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 …

A bstract Hadronization is a critical step in the simulation of high-energy particle and nuclear
physics experiments. As there is no first principles understanding of this process, physically …

Unfolding is an important procedure in particle physics experiments that corrects for detector
effects and provides differential cross section measurements that can be used for a number …