Deep learning as a parton shower

A bstract We make the connection between certain deep learning architectures and the renormalisation group explicit in the context of QCD by using a deep learning network to construct a toy parton shower model. The model aims to describe proton-proton collisions at the Large Hadron Collider. A convo...

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Veröffentlicht in:The journal of high energy physics 2018-12, Vol.2018 (12), p.1-26, Article 21
1. Verfasser: Monk, J. W.
Format: Artikel
Sprache:eng
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Zusammenfassung:A bstract We make the connection between certain deep learning architectures and the renormalisation group explicit in the context of QCD by using a deep learning network to construct a toy parton shower model. The model aims to describe proton-proton collisions at the Large Hadron Collider. A convolutional autoencoder learns a set of kernels that efficiently encode the behaviour of fully showered QCD collision events. The network is structured recursively so as to ensure self-similarity, and the number of trained network parameters is low. Randomness is introduced via a novel custom masking layer, which also preserves existing parton splittings by using layer-skipping connections. By applying a shower merging procedure, the network can be evaluated on unshowered events produced by a matrix element calculation. The trained network behaves as a parton shower that qualitatively reproduces jet-based observables.
ISSN:1029-8479
1029-8479
DOI:10.1007/JHEP12(2018)021