Phase space sampling and inference from weighted events with autoregressive flows
Bob Stienen, Rob Verheyen
SciPost Phys. 10, 038 (2021) · published 17 February 2021
- doi: 10.21468/SciPostPhys.10.2.038
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Abstract
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.
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Authors / Affiliations: mappings to Contributors and Organizations
See all Organizations.- 1 Bob Stienen,
- 2 Rob Verheyen
- 1 Radboud Universiteit Nijmegen / Radboud University Nijmegen [RUN]
- 2 University College London [UCL]
Funders for the research work leading to this publication