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 …

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

We introduce a diffusion-based generative model to describe the distribution of galaxies in
our Universe directly as a collection of points in 3D space (coordinates) optionally with …

This paper presents a novel approach for directly generating full events at detector-level
from parton-level information, leveraging cutting-edge machine learning techniques. To …

Denoising diffusion models have gained prominence in various generative tasks, prompting
their exploration for the generation of calorimeter responses. Given the computational …

A bstract We introduce a new technique called D rapes to enhance the sensitivity in
searches for new physics at the LHC. By training diffusion models on side-band data, we …

Generative artificial intelligence is a fast-growing area of research offering various avenues
for exploration in high-energy nuclear physics. In this work, we explore the use of generative …

Off-shell effects in large LHC backgrounds are crucial for precision predictions and, at the
same time, challenging to simulate. We present a novel method to transform high …

Recently, combinations of generative and Bayesian deep learning have been introduced in
particle physics for both fast detector simulation and inference tasks. These neural networks …