Event generation for the LHC can be supplemented by generative adversarial networks,
which generate physical events and avoid highly inefficient event unweighting. For top pair …

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 …

LHC analyses directly comparing data and simulated events bear the danger of using first-
principle predictions only as a black-box part of event simulation. We show how simulations …

A critical question concerning generative networks applied to event generation in particle
physics is if the generated events add statistical precision beyond the training sample. We …

We explore the use of autoregressive flows, a type of generative model with tractable
likelihood, as a means of efficient generation of physical particle collider events. The usual …

We present a novel approach for the integration of scattering cross sections and the
generation of partonic event samples in high-energy physics. We propose an importance …

Abstract The algorithm for Monte Carlo simulation of parton-level events based on an
Artificial Neural Network (ANN) proposed in arXiv: 1810.11509 is used to perform a …

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 …

Subtracting event samples is a common task in LHC simulation and analysis, and standard
solutions tend to be inefficient. We employ generative adversarial networks to produce new …

Fast data generation based on Machine Learning has become a major research topic in
particle physics. This is mainly because the Monte Carlo simulation approach is …