Neural network-based approach to phase space integration
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 se...
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| Format: | Article |
| Language: | English |
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SciPost
2020-10-01
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| Series: | SciPost Physics |
| Online Access: | https://scipost.org/SciPostPhys.9.4.053 |
| Summary: | 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. |
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| ISSN: | 2542-4653 |