Improved Neural Network Monte Carlo Simulation
This work addresses simulation efficiency for particle physics researchers, but it is incremental as it builds on prior methods with specific improvements.
The authors tackled the problem of Monte Carlo simulation for particle physics events by improving an existing neural network training algorithm to avoid numerical instabilities, achieving an integrated decay width within 0.7% of the true value and an unweighting efficiency of 26% for the H→4ℓ decay.
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.