23–28 Oct 2022
Villa Romanazzi Carducci, Bari, Italy
Europe/Rome timezone

Loop Amplitudes from Precision Networks

25 Oct 2022, 14:50
20m
Sala A+A1 (Villa Romanazzi)

Sala A+A1

Villa Romanazzi

Oral Track 3: Computations in Theoretical Physics: Techniques and Methods Track 3: Computations in Theoretical Physics: Techniques and Methods

Speaker

Anja Butter

Description

Evaluating loop amplitudes is a time-consuming part of LHC event generation. For di-photon production with jets we show that simple, Bayesian networks can learn such amplitudes and model their uncertainties reliably. A boosted training of the Bayesian network further improves the uncertainty estimate and the network precision in critical phase space regions. In general, boosted network training of Bayesian networks allows us to move between fit-like and interpolation-like regimes of network training.

References

https://arxiv.org/abs/2206.14831

Previous publications:
https://arxiv.org/abs/2110.13632
https://arxiv.org/abs/2106.09474

Significance

For the first time we integrate uncertainties in the training process of Bayesian neural networks for the prediction of amplitudes. This allows a boosting for performance and reliability of the predicted amplitudes.

Primary authors

Anja Butter Michel Luchmann (Universität Heidelberg) Sebastian Pitz (ITP, Universität Heidelberg) Simon David Badger (Universita e INFN Torino (IT)) Tilman Plehn

Presentation materials