Publications Details

Developing and applying quantifiable metrics for diagnostic and experiment design on Z

Laros, James H.; Knapp, Patrick F.; Beckwith, Kristian B.; Evstatiev, Evstati G.; Fein, Jeffrey R.; Jennings, Christopher A.; Joseph, Roshan; Klein, Brandon T.; Maupin, Kathryn A.; Nagayama, Taisuke N.; Patel, Ravi G.; Schaeuble, Marc-Andre S.; Vasey, Gina; Ampleford, David A.

This project applies methods in Bayesian inference and modern statistical methods to quantify the value of new experimental data, in the form of new or modified diagnostic configurations and/or experiment designs. We demonstrate experiment design methods that can be used to identify the highest priority diagnostic improvements or experimental data to obtain in order to reduce uncertainties on critical inferred experimental quantities and select the best course of action to distinguish between competing physical models. Bayesian statistics and information theory provide the foundation for developing the necessary metrics, using two high impact experimental platforms on Z as exemplars to develop and illustrate the technique. We emphasize that the general methodology is extensible to new diagnostics (provided synthetic models are available), as well as additional platforms. We also discuss initial scoping of additional applications that began development in the last year of this LDRD.