The full optimization of the design and operation of instruments whose functioning relies on
the interaction of radiation with matter is a super-human task, due to the large dimensionality …

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

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 …

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 …

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 …

Abstract The Search for Hidden Particles (SHiP) Collaboration has proposed a general-
purpose experimental facility operating in beam-dump mode at the CERN SPS accelerator …

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 …

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 …

The computational cost for high energy physics detector simulation in future experimental
facilities is going to exceed the current available resources. To overcome this challenge …