In modern collider experiments, the quest to explore fundamental interactions between
elementary particles has reached unparalleled levels of precision. Signatures from particle …

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 …

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 …

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 propose masked particle modeling (MPM) as a self-supervised method for learning
generic, transferable, and reusable representations on unordered sets of inputs for use in …

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 …

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

Fast simulation of the energy depositions in high-granular detectors is needed for future
collider experiments at ever-increasing luminosities. Generative machine learning (ML) …

Jets at the LHC, typically consisting of a large number of highly correlated particles, are a
fascinating laboratory for deep generative modeling. In this paper, we present two novel …