High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into
open questions in particle physics. However, detector effects must be corrected before …

In this paper, we present a new method to efficiently generate jets in High Energy Physics
called PC-JeDi. This method utilises score-based diffusion models in conjunction with …

The first measurement of lepton-jet momentum imbalance and azimuthal correlation in
lepton-proton scattering at high momentum transfer is presented. These data, taken with the …

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 …

Machine learning-based unfolding has enabled unbinned and high-dimensional differential
cross section measurements. Two main approaches have emerged in this research area; …

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 …

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 …

We introduce CaloFlow, a fast detector simulation framework based on normalizing flows.
For the first time, we demonstrate that normalizing flows can reproduce many-channel …

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 …