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Improved Neural Network Monte Carlo Simulation
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abstract
The algorithm for Monte Carlo simulation of parton-level events based on an Artificial Neural Network (ANN) proposed in arXiv:1810.11509 is used to perform a simulation of $H\to 4\ell$ decay. Improvements in the training algorithm have been implemented to avoid numerical instabilities. The integrated decay width evaluated by the ANN is within 0.7% of the true value and unweighting efficiency of 26% is reached. While the ANN is not automatically bijective between input and output spaces, which can lead to issues with simulation quality, we argue that the training procedure naturally prefers bijective maps, and demonstrate that the trained ANN is bijective to a very good approximation.
Forward citations
Cited by 2 Pith papers
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FASTColor -- Full-color Amplitude Surrogate Toolkit for QCD
An ML surrogate for the leading-to-full-color reweighting factor accelerates QCD event generation by up to a factor of two while preserving full-color accuracy.
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LeStrat-Net: Lebesgue style stratification for Monte Carlo simulations powered by machine learning
A neural network learns isocontour-defined partition regions of an integrand, providing cheap region classification and volume estimates for stratified Monte Carlo integration and event unweighting.
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