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One-loop matrix element emulation with factorisation awareness

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arxiv 2302.04005 v2 pith:FJAZJ2I4 submitted 2023-02-08 hep-ph

classification hep-ph
keywords matrixaccuracyelementsone-loopemulationfactorisationstrategytraining
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In this article we present an emulation strategy for one-loop matrix elements. This strategy is based on the factorisation properties of matrix elements and is an extension of the work presented in arXiv:2107.06625. We show that a percent-level accuracy can be achieved even for large multiplicity processes. The point accuracy obtained is such that it dwarfs the statistical accuracy of the training sample which allows us to use our model to augment the size of the training set by orders of magnitude without additional evaluations of expensive one-loop matrix elements.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Generative Amplification with Surrogate Monte Carlo

    hep-ph 2026-08 conditional novelty 6.0 of 10

    An amplitude surrogate trained on a few thousand exact LHC amplitude points statistically outperforms the training data, with largest amplification in sparsely populated kinematic tails of Z+g and Z+4g production.

  2. A Novel Implementation of the Matrix Element Method at Next-to-Leading Order for the Measurement of the Higgs Self-Coupling ${\lambda}_{3H}$

    hep-ph 2026-02 conditional novelty 6.0 of 10

    A new POWHEG–MoMEMta interface and 'Block N' phase-space block realize the first MEM@NLO for gg→HH→bbγγ, recovering κλ=1 within ~0.5 expected uncertainty on Monte Carlo pseudo-experiments.

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