2020/11/30 by Bob Stienen, Rob Verheyen
Computer Science · Physics and Astronomy · #Autoregressive model #Collider #Event (particle physics) #Function (biology) #Gaussian Processes and Bayesian Inference #High-Energy Particle Collisions Research #Importance sampling #Inference #Particle physics theoretical and experimental studies #Phase space #Production (economics) #Sampling (signal processing) #hep-ph
paper · pdf · doi:10.21468/scipostphys.10.2.038
published as SciPost Phys. 10, 038 (2021) · 26 pages, 7 figures
openalex created_date 2020/12/07 · arxiv created 2021/01/19 · arxiv updated 2021/02/17 · openalex publication_date 2021/02/17 · openalex updated_date 2026/08/05
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 maximum likelihood loss function is supplemented by an event weight, allowing for inference from event samples with variable, and even negative event weights. To illustrate the efficacy of the model, we perform experiments with leading-order top pair production events at an electron collider with importance sampling weights, and with next-to-leading-order top pair production events at the LHC that involve negative weights.