mlsanityData to 7 Oct 2026

Optimising simulations for diphoton production at hadron colliders using amplitude neural networks

arXiv 2106.09474 ↗First posted 17 Jun 2021hep-ph cs.AI cs.LGcited by 9

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Abstract (arXiv)

Machine learning technology has the potential to dramatically optimise event generation and simulations. We continue to investigate the use of neural networks to approximate matrix elements for high-multiplicity scattering processes. We focus on the case of loop-induced diphoton production through gluon fusion and develop a realistic simulation method that can be applied to hadron collider observables. Neural networks are trained using the one-loop amplitudes implemented in the NJet C++ library and interfaced to the Sherpa Monte Carlo event generator where we perform a detailed study for 2→3 and 2→4 scattering problems. We also consider how the trained networks perform when varying the kinematic cuts effecting the phase space and the reliability of the neural network simulations.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 15 of its 96 references that are in the map. First posted on arXiv 17 Jun 2021.

Builds on

15 of 96 references.

References · 15

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3 more
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  3. 2021The use of Generative Adversarial Networks to characterise new physics in multi-lepton final states at the LHCcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 3; 2025: 3; 2026: 2.

9 papers.

  1. 2024Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physicscited by 36
  2. 2024A Lorentz-Equivariant Transformer for All of the LHCcited by 27
  3. 2025Lorentz Local Canonicalization: How to Make Any Network Lorentz-Equivariantcited by 12
  4. 2026Scaling laws for amplitude surrogatescited by 5
  5. 2025Forecasting Generative Amplificationcited by 3
  6. 2024Extrapolating Jet Radiation with Autoregressive Transformerscited by 2
  7. 2021Comparing Machine Learning and Interpolation Methods for Loop-Level Calculationscited by 1
  8. 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
  9. 2026Event Generation with Parallel Langevin Sampling and Learned Stein Diagnosticscited by 0