SciPost Physics Core

SciPost Phys. Core 3, 009 (2020) · published 5 November 2020

• doi: 10.21468/SciPostPhysCore.3.2.009
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Abstract

Subtracting event samples is a common task in LHC simulation and analysis, and standard solutions tend to be inefficient. We employ generative adversarial networks to produce new event samples with a phase space distribution corresponding to added or subtracted input samples. We first illustrate for a toy example how such a network beats the statistical limitations of the training data. We then show how such a network can be used to subtract background events or to include non-local collinear subtraction events at the level of unweighted 4-vector events.

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