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arXiv · 1912.08824

How to GAN Event Subtraction

Anja Butter·Tilman Plehn·Ramon Winterhalder
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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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Anja Butter, Tilman Plehn, Ramon Winterhalder. 2019-12-18. How to GAN Event Subtraction. https://doi.org/10.21468/scipostphyscore.3.2.009

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