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Efficient negative-weight elimination in large high-multiplicity Monte Carlo event samples

Andersen, Jeppe R.; Maier, Andreas; Maître, Daniel

Efficient negative-weight elimination in large high-multiplicity Monte Carlo event samples Thumbnail


Authors

Andreas Maier



Abstract

We demonstrate that cell resampling can eliminate the bulk of negative event weights in large event samples of high multiplicity processes without discernible loss of accuracy in the predicted observables. The application of cell resampling to much larger data sets and higher multiplicity processes such as vector boson production with up to five jets has been made possible by improvements in the method paired with drastic enhancement of the computational efficiency of the implementation.

Citation

Andersen, J. R., Maier, A., & Maître, D. (2023). Efficient negative-weight elimination in large high-multiplicity Monte Carlo event samples. The European Physical Journal C, 83(9), Article 835. https://doi.org/10.1140/epjc/s10052-023-11905-0

Journal Article Type Article
Acceptance Date Aug 4, 2023
Online Publication Date Sep 21, 2023
Publication Date 2023
Deposit Date Oct 19, 2023
Publicly Available Date Oct 23, 2023
Journal The European Physical Journal C
Print ISSN 1434-6044
Electronic ISSN 1434-6052
Publisher SpringerOpen
Peer Reviewed Peer Reviewed
Volume 83
Issue 9
Article Number 835
DOI https://doi.org/10.1140/epjc/s10052-023-11905-0
Public URL https://durham-repository.worktribe.com/output/1806984

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Licence
http://creativecommons.org/licenses/by/4.0/

Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.





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