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

Many particle physics datasets like those generated at colliders are described by continuous
coordinates (in contrast to grid points like in an image), respect a number of symmetries (like …

Score-based generative models are a new class of generative algorithms that have been
shown to produce realistic images even in high dimensional spaces, currently surpassing …

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

There has been a recent explosion in research into machine-learning-based generative
modeling to tackle computational challenges for simulations in high energy physics (HEP) …

We explore the use of normalizing flows to emulate Monte Carlo detector simulations of
photon showers in a high-granularity electromagnetic calorimeter prototype for the …

Simulating showers of particles in highly-granular detectors is a key frontier in the
application of machine learning to particle physics. Achieving high accuracy and speed with …

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 …

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 …