Improved neural network Monte Carlo simulation
I-Kai Chen, Matthew Klimek, Maxim Perelstein
Abstract
The algorithm for Monte Carlo simulation of parton-level events based on an Artificial Neural Network (ANN) proposed in Ref.~ is used to perform a simulation of H\to 4\ell <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow> <mml:mi>H</mml:mi> <mml:mo>→</mml:mo> <mml:mn>4</mml:mn> <mml:mi>ℓ</mml:mi> </mml:mrow> </mml:math> 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.