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The MadNIS Reloaded

by Theo Heimel, Nathan Huetsch, Fabio Maltoni, Olivier Mattelaer, Tilman Plehn, Ramon Winterhalder

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Submission summary

Authors (as registered SciPost users): Theo Heimel · Nathan Huetsch · Tilman Plehn · Ramon Winterhalder
Submission information
Preprint Link: https://arxiv.org/abs/2311.01548v3  (pdf)
Date accepted: 2024-06-19
Date submitted: 2024-06-03 07:25
Submitted by: Winterhalder, Ramon
Submitted to: SciPost Physics
Ontological classification
Academic field: Physics
Specialties:
  • High-Energy Physics - Phenomenology
Approach: Computational

Abstract

In pursuit of precise and fast theory predictions for the LHC, we present an implementation of the MadNIS method in the MadGraph event generator. A series of improvements in MadNIS further enhance its efficiency and speed. We validate this implementation for realistic partonic processes and find significant gains from using modern machine learning in event generators.

Author indications on fulfilling journal expectations

  • Provide a novel and synergetic link between different research areas.
  • Open a new pathway in an existing or a new research direction, with clear potential for multi-pronged follow-up work
  • Detail a groundbreaking theoretical/experimental/computational discovery
  • Present a breakthrough on a previously-identified and long-standing research stumbling block

Author comments upon resubmission

Dear referees,

We would like to thank all the referees for their detailed and careful comments on our manuscript that helped us to further improve the presentation of our research results. The individual comments raised by the refeeres have been answered/commented directly (see previous submission)

List of changes

see above and comments to previous submission

Published as SciPost Phys. 17, 023 (2024)

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