For simulations where the forward and the inverse directions have a physics meaning,
invertible neural networks are especially useful. A conditional INN can invert a detector …

We introduce two diffusion models and an autoregressive transformer for LHC physics
simulations. Bayesian versions allow us to control the networks and capture training …

QCD splittings are among the most fundamental theory concepts at the LHC. We show how
they can be studied systematically with the help of invertible neural networks. These …

LHC physics crucially relies on our ability to simulate events efficiently from first principles.
Modern machine learning, specifically generative networks, will help us tackle simulation …

Whenever invertible generative networks are needed for LHC physics, normalizing flows
show excellent performance. A challenge is their scaling to high-dimensional phase spaces …

Searches for anomalies are a significant motivation for the LHC and help define key analysis
steps, including triggers. We discuss specific examples how LHC anomalies can be defined …

Event generation with neural networks has seen significant progress recently. The big open
question is still how such new methods will accelerate LHC simulations to the level required …

The large data rates at the LHC require an online trigger system to select relevant collisions.
Rather than compressing individual events, we propose to compress an entire data set at …

Tabular data stands out as one of the most frequently encountered types in high energy
physics. Unlike commonly homogeneous data such as pixelated images, simulating high …

The LHC produces huge amounts of data in which signs of new physics can be hidden. To
take full advantage of existing or future LHC data, it is worth exploring novel techniques such …