Stienen, B;
Verheyen, R;
(2021)
Phase space sampling and inference from weighted events with autoregressive flows.
SciPost Physics
, 10
(2)
, Article 038. 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.
| Type: | Article |
|---|---|
| Title: | Phase space sampling and inference from weighted events with autoregressive flows |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.21468/SCIPOSTPHYS.10.2.038 |
| Publisher version: | https://doi.org/10.21468/SCIPOSTPHYS.10.2.038 |
| Language: | English |
| Additional information: | © SciPost. This work is licensed under the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10127923 |
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