Improved Neural Network Monte Carlo Simulation

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.

19 pages, 11 figures; v2: minor clarifications, results unchanged, 21 pages

References in corpus (5)

• Precise predictions for the Higgs-boson decay H -> W W/Z Z -> 4 leptons
• Measurements of the Higgs boson width and anomalous HVV couplings from on-shell and off-shell production in the four-lepton final state
• Higgs boson production cross-section measurements and their EFT interpretation in the $4\ell$ decay channel at $\sqrt{s}$ = 13 TeV with the ATLAS detector
• A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties
• Measurements of the Higgs boson inclusive and differential fiducial cross sections in the 4$\ell$ decay channel at $\sqrt{s}$ = 13 TeV

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