Diffusion models are generative models, which gradually add and remove noise to learn the
underlying distribution of training data for data generation. The components of diffusion …

Simulation is crucial for all aspects of collider data analysis, but the available computing
budget in the High Luminosity LHC era will be severely constrained. Generative machine …

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 …

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 …

Building on the success of PC-JeDi we introduce PC-Droid, a substantially improved
diffusion model for the generation of jet particle clouds. By leveraging a new diffusion …

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 …

Differentiable programming opens exciting new avenues in particle physics, also affecting
future event generators. These new techniques boost the performance of current and …

Building on the success of PC-JeDi we introduce PC-Droid, a substantially improved
diffusion model for the generation of jet particle clouds. By leveraging a new diffusion …