Deep learning as a parton shower

• Main Author: J. W. Monk
• Format: Article
• Language: English
• Published: SpringerOpen 2018-12-01
• Series: Journal of High Energy Physics
• Subjects: Phenomenological Models; Jets
• Online Access: http://link.springer.com/article/10.1007/JHEP12(2018)021

Abstract 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.