1,431 research outputs found
Deep Learning as a Parton Shower
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.Comment: 26 pages, 13 figure
To live and die in CA
This thesis investigates the nature of elementary cellular automata to better understand their relationship of the models they support to the biological organisms that create the mats and soil crusts found in extreme environments here on earth. Cellular automata have been used to study growth and patterns in forests, arid desert environments, predator-prey problems, and sea shells. It has also been used to study areas of diverse epidemiology and linguistics. Cellular automata have been used as the core of computer games as well. This investigation has led to develop a graphical grammar for simple cellular automata, using L-systems, a grammar system designed by a biologist, Aristid Lindenmayer, to describe growth in biological systems. Also discussed is scaling algorithms, and the associated variable dependencies that create them. All of the cellular automata investigated in this thesis are totalistic (they update simultaneously). Random updating of cells in models to simulate the random availability of resources (water and nutrients) could be especially useful in models of resource limited ecologies like deserts, the artic, and even Mars
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