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(Machine) Learning amplitudes for faster event generation

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arxiv 1912.11055 v2 pith:5UWRYCMO submitted 2019-12-23 hep-ph

Fady Bishara , Marc Montull This is my paper · ORCID
classification hep-ph
keywords amplitudeseventevaluationexactfastergeneratorslearningmachine
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

We propose to replace the exact amplitudes used in MC event generators for trained Machine Learning regressors, with the aim of speeding up the evaluation of {\it slow} amplitudes. As a proof of concept, we study the process $gg \to ZZ$ whose LO amplitude is loop induced. We show that gradient boosting machines like $\texttt{XGBoost}$ can predict the fully differential distributions with errors below $0.1 \%$, and with prediction times $\mathcal{O}(10^3)$ faster than the evaluation of the exact function. This is achieved with training times $\sim 7$ minutes and regressors of size $\lesssim 30$~Mb. These results suggest a possible new avenue to speed up MC event generators.

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Cited by 3 Pith papers

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