SciPost Phys. 10, 023 (2021) ·
published 29 January 2021
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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.
SciPost Phys. 9, 053 (2020) ·
published 19 October 2020
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Monte Carlo methods are widely used in particle physics to integrate and
sample probability distributions (differential cross sections or decay rates)
on multi-dimensional phase spaces. We present a Neural Network (NN) algorithm
optimized to perform this task. The algorithm has been applied to several
examples of direct relevance for particle physics, including situations with
non-trivial features such as sharp resonances and soft/collinear enhancements.
Excellent performance has been demonstrated in all examples, with the properly
trained NN achieving unweighting efficiencies of between 30% and 75%. In
contrast to traditional Monte Carlo algorithms such as VEGAS, the NN-based
approach does not require that the phase space coordinates be aligned with
resonant or other features in the cross section.
