Authors:
(1) Cameron Parker, Cyclotron Institute, Texas A&M University and Department of Physics and Astronomy, Texas A&M University (E-mail: [email protected]);
(2) JETSCAPE Collaboration.
Table of Links
2. Vacuum Systems
The Bayesian analysis process begins with creating a starting set of design points within a parameter space given by the prior ranges for each parameter. Our prior distribution of parameters is assumed flat within parameters space, and we use a Latin hypercube to generate these points. They will be used to run JETSCAPE. A Gaussian process emulator is utilized to generate observables between the design points in parameter space. The observables produced are then compared to data. A Markov chain Monte Carlo determines new sets of points that improve the description. This process is repeated until convergence, giving us the posterior distribution. The posterior distributions for our set of parameters are shown in Fig. 1 together with correlations between pairs of parameters. The observables for the posterior distribution are shown in Fig. 2.
![Figure 2: Generated data from the posterior compared to observables. We see a very strong agreement with the jet data and an agreement with hadron data that improves as 𝑝𝑇 increases. We compare to CMS data [10, 11] for LHC energy and PHENIX data [12] for RHIC energy](https://hackernoon.imgix.net/images/fWZa4tUiBGemnqQfBGgCPf9594N2-7mb30z3.png?auto=format&fit=max&w=3840)
This paper is available on arxiv under CC BY 4.0 DEED license.
