Development of Bayesian analysis program for extraction of polarisation observables at CLAS

Lewis, S., Ireland, D. and Vanderbauwhede, W. (2014) Development of Bayesian analysis program for extraction of polarisation observables at CLAS. Journal of Physics: Conference Series, 513, 022020. (doi: 10.1088/1742-6596/513/2/022020)

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At the mass scale of a proton, the strong force is not well understood. Various quark models exist, but it is important to determine which quark model(s) are most accurate. Experimentally, finding resonances predicted by some models and not others would give valuable insight into this fundamental interaction. Several labs around the world use photoproduction experiments to find these missing resonances. The aim of this work is to develop a robust Bayesian data analysis program for extracting polarisation observables from pseudoscalar meson photoproduction experiments using CLAS at Jefferson Lab. This method, known as nested sampling, has been compared to traditional methods and has incorporated data parallelisation and GPU programming. It involves an event-by-event likelihood function, which has no associated loss of information from histogram binning, and results can be easily constrained to the physical region. One of the most important advantages of the nested sampling approach is that data from different experiments can be combined and analysed simultaneously. Results on both simulated and previously analysed experimental data for the K+Λ channel will be discussed.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Vanderbauwhede, Professor Wim and Ireland, Professor David
Authors: Lewis, S., Ireland, D., and Vanderbauwhede, W.
College/School:College of Science and Engineering > School of Computing Science
College of Science and Engineering > School of Physics and Astronomy
Journal Name:Journal of Physics: Conference Series
Publisher:IOP Publishing
ISSN (Online):1742-6596
Copyright Holders:Copyright © 2014 IOP Publishing
First Published:First published in Journal of Physics: Conference Series 513:022020
Publisher Policy:Reproduced under a creative commons license

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