Modern Bayesian inference involves a mixture of computational techniques for estimating,
validating, and drawing conclusions from probabilistic models as part of principled …

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 …

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 …

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

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 …

Generative networks are opening new avenues in fast event generation for the LHC. We
show how generative flow networks can reach percent-level precision for kinematic …

This work proposes “jointly amortized neural approximation”(JANA) of intractable likelihood
functions and posterior densities arising in Bayesian surrogate modeling and simulation …

Normalizing Flows are generative models that directly maximize the likelihood. Previously,
the design of normalizing flows was largely constrained by the need for analytical …

The matrix element method is widely considered the ultimate LHC inference tool for small
event numbers. We show how a combination of two conditional generative neural networks …

Abstract Theory predictions for the LHC require precise numerical phase-space integration
and generation of unweighted events. We combine machine-learned multi-channel weights …