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Register a new userA survey of machine learning-based physics event generation
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Added by
Wally Melnitchouk
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Event generators in high-energy nuclear and particle physics play an important role in facilitating studies of particle reactions. We survey the state-of-the-art of machine learning (ML) efforts at building physics event generators. We review ML generative models used in ML-based event generators and their specific challenges, and discuss various approaches of incorporating physics into the ML model designs to overcome these challenges. Finally, we explore some open questions related to super-resolution, fidelity, and extrapolation for physics event generation based on ML technology.
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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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