A Continuous Normalizing Flow trained with Flow Matching improves unweighting efficiency in high-multiplicity Drell-Yan and top-pair event generation by factors of 150 and 17 over Vegas.
HL-LHC Computing Review Stage-2, Common Software Projects: Event Generators
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abstract
This paper has been prepared by the HEP Software Foundation (HSF) Physics Event Generator Working Group (WG), as an input to the second phase of the LHCC review of High-Luminosity LHC (HL-LHC) computing, which is due to take place in November 2021. It complements previous documents prepared by the WG in the context of the first phase of the LHCC review in 2020, including in particular the WG paper on the specific challenges in Monte Carlo event generator software for HL-LHC, which has since been updated and published, and which we are also submitting to the November 2021 review as an integral part of our contribution.
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Efficient many-jet event generation with Flow Matching
A Continuous Normalizing Flow trained with Flow Matching improves unweighting efficiency in high-multiplicity Drell-Yan and top-pair event generation by factors of 150 and 17 over Vegas.