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 …

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

We describe the outcome of a data challenge conducted as part of the Dark Machines
Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged …

Abstract A Generative-Adversarial Network (GAN) based on convolutional neural networks
is used to simulate the production of pairs of jets at the LHC. The GAN is trained on events …

In many fields of science, high-dimensional integration is required. Numerical methods have
been developed to evaluate these complex integrals. We introduce the code i-flow, a Python …

Accurate simulation of physical processes is crucial for the success of modern particle
physics. However, simulating the development and interaction of particle showers with …

Abstract Machine learning techniques in particle physics are most powerful when they are
trained directly on data, to avoid sensitivity to theoretical uncertainties or an underlying bias …

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 …

Motivated by the computational limitations of simulating interactions of particles in highly-
granular detectors, there exists a concerted effort to build fast and exact machine-learning …

Simulating high-resolution detector responses is a storage-costly and computationally
intensive process that has long been challenging in particle physics. Despite the ability of …