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 …

Simulations play a key role for inference in collider physics. We explore various approaches
for enhancing the precision of simulations using machine learning, including interventions at …

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

We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields state-
of-the-art performance for a wide range of machine learning tasks at the Large Hadron …

The rapid deployment of computing hardware different from the traditional CPU+ RAM
model in data centers around the world mandates a change in the design of event …

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, several normalizing flow-based deep generative models have been proposed to
accelerate the simulation of calorimeter showers. Using caloflow as an example, we show …

It is fascinating to predict the mass and width of the ordinary and exotic mesons solely based
on their quark content and quantum numbers. Such prediction goes beyond conventional …