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

@article{AylettBullock2021OptimisingSF,
  title={Optimising simulations for diphoton production at hadron colliders using amplitude neural networks},
  author={Joseph Aylett-Bullock and Simon Badger and Ryan I. Moodie},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.09474}
}
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… 

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