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The algorithm for Monte Carlo simulation of parton-level events basedon an Artificial Neural Network (ANN) proposed in Ref.~ is used toperform a simulation of H\to 4\ell H → 4 ℓ decay. Improvements in the training algorithm have been implemented toavoid numerical instabilities. The integrated decay width evaluated bythe ANN is within 0.7% of the true value and unweighting efficiency of26% is reached. While the ANN is not automatically bijective betweeninput and output spaces, which can lead to issues with simulationquality, we argue that the training procedure naturally prefersbijective maps, and demonstrate that the trained ANN is bijective to avery good approximation.
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