Dimensionally constrained energy confinement analysis of W7-AS data

A recently assembled W7-AS stellarator database has been subject to dimensionally constrained confinement analysis. The analysis employs Bayesian inference. Dimensional information is taken from the Conner-Taylor (CT) similarity transformation theory, which provides six possible physical scenarios with associated dimensional conditions. Bayesian theory allows the calculation of the probability for each model and it is found that the present W7-AS data are most probably described by the collisionless high-beta case. Probabilities for all models and the associated exponents of a power law scaling function are presented.