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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2020-10-01
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| Series: | SciPost Physics |
| Online Access: | https://scipost.org/SciPostPhys.9.4.053 |
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doaj-5335e0d385ae44aea11f81afdc308fc72020-11-25T03:41:37ZengSciPostSciPost Physics2542-46532020-10-019405310.21468/SciPostPhys.9.4.053Neural network-based approach to phase space integrationMatthew D. Klimek, Maxim PerelsteinMonte 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.https://scipost.org/SciPostPhys.9.4.053 |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Matthew D. Klimek, Maxim Perelstein |
| spellingShingle |
Matthew D. Klimek, Maxim Perelstein Neural network-based approach to phase space integration SciPost Physics |
| author_facet |
Matthew D. Klimek, Maxim Perelstein |
| author_sort |
Matthew D. Klimek, Maxim Perelstein |
| title |
Neural network-based approach to phase space integration |
| title_short |
Neural network-based approach to phase space integration |
| title_full |
Neural network-based approach to phase space integration |
| title_fullStr |
Neural network-based approach to phase space integration |
| title_full_unstemmed |
Neural network-based approach to phase space integration |
| title_sort |
neural network-based approach to phase space integration |
| publisher |
SciPost |
| series |
SciPost Physics |
| issn |
2542-4653 |
| publishDate |
2020-10-01 |
| description |
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. |
| url |
https://scipost.org/SciPostPhys.9.4.053 |
| work_keys_str_mv |
AT matthewdklimekmaximperelstein neuralnetworkbasedapproachtophasespaceintegration |
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1724529163081089024 |