November 29, 2021 to December 3, 2021
Virtual and IBS Science Culture Center, Daejeon, South Korea
Asia/Seoul timezone

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

contribution ID 567
Dec 1, 2021, 5:00 PM
20m
S305 (Virtual and IBS Science Culture Center)

S305

Virtual and IBS Science Culture Center

55 EXPO-ro Yuseong-gu Daejeon, South Korea email: library@ibs.re.kr +82 42 878 8299
Oral Track 3: Computations in Theoretical Physics: Techniques and Methods Track 3: Computations in Theoretical Physics: Techniques and Methods

Speaker

Ryan Moodie (IPPP, Durham University)

Description

Phenomenological studies of high-multiplicity scattering processes at collider experiments present a substantial theoretical challenge and are increasingly important ingredients in experimental measurements. We investigate the use of neural networks to approximate matrix elements for these processes, studying the case of loop-induced diphoton production through gluon fusion. We train neural network models on one-loop amplitudes from the NJet library and interface them with the Sherpa Monte Carlo event generator to provide the matrix element within a realistic hadronic collider simulation. Computing some standard observables with the models and comparing to conventional techniques, we find excellent agreement in the distributions and a reduced simulation time by a factor of 30.

Significance

We extend previous work which pioneered the emulation of scattering amplitudes with neural networks, studying these techniques for the first time within a full hadronic collider simulation.

References

https://arxiv.org/abs/2106.09474
https://doi.org/10.1007/JHEP06(2020)114

Speaker time zone Compatible with Europe

Primary authors

Joseph Bullock (IPPP, Durham University) Ryan Moodie (IPPP, Durham University) Simon Badger (Università degli Studi di Torino)

Presentation materials