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

20th International Conference on Computing in High Energy and Nuclear Physics

物理学;计算机科学

Lewis, S.^1 ; Ireland, D.^1 ; Vanderbauwhede, W.^1

SUPA, School of Physics and Astronomy, University of Glasgow, Glasgow

G12 8QQ, United Kingdom^1

关键词: Bayesian Analysis;    Bayesian data analysis;    GPU programming;    Likelihood functions;    Meson photoproduction;    Parallelisation;    Photoproductions;    Physical region;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/513/2/022020/pdf
DOI  :  10.1088/1742-6596/513/2/022020

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

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.