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

We leverage recent breakthroughs in neural density estimation to propose a new
unsupervised ANOmaly detection with Density Estimation (ANODE) technique. By …

Deep learning tools can incorporate all of the available information into a search for new
particles, thus making the best use of the available data. This paper reviews how to optimally …

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 present a novel integrator based on normalizing flows which can be used to improve the
unweighting efficiency of Monte Carlo event generators for collider physics simulations. In …

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 …

We introduce CaloFlow, a fast detector simulation framework based on normalizing flows.
For the first time, we demonstrate that normalizing flows can reproduce many-channel …

A bstract Anomaly detection relies on designing a score to determine whether a particular
event is uncharacteristic of a given background distribution. One way to define a score is to …

Collider searches face the challenge of defining a representation of high-dimensional data
such that physical symmetries are manifest, the discriminating features are retained, and the …