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arXiv 2106.09474hep-phcs.AIcs.LG
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Optimising simulations for diphoton production at hadron colliders using amplitude neural networks

Durham University(杜伦大学);Institute for Particle Physics Phenomenology(粒子物理现象学研究所);Institute for Data Science(数据科学研究所);Università di Torino(都灵大学);INFN(意大利国家核物理研究院)

Joseph Aylett-Bullock, Simon Badger, Ryan Moodie

英文摘要:

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 $2\to3$ and $2\to4$ 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.

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