The JET hybrid scenario has been developed from low plasma current carbon wall discharges to the record-breaking Deuterium-Tritium plasmas obtained in 2021 with the ITER-like Be/W wall. The development started in pure Deuterium with refinement of the plasma current, and toroidal magnetic field choices and succeeded in solving the heat load challenges arising from 37 MW of injected power in the ITER like wall environment, keeping the radiation in the edge and core controlled, avoiding MHD instabilities and reaching high neutron rates. The Deuterium hybrid plasmas have been re-run in Tritium and methods have been found to keep the radiation controlled but not at high fusion performance probably due to time constraints. For the first time this scenario has been run in Deuterium-Tritium (50:50). These plasmas were re-optimised to have a radiation-stable H-mode entry phase, good impurity control through edge T_i gradient screening and optimised performance with fusion power exceeding 10 MW for longer than three alpha particle slow down times, 8.3 MW averaged over 5 s and fusion energy of 45.8 MJ.

The Bayesian information criterion (BIC), the Akaike information criterion (AIC), and some other indicators derived from them are widely used for model selection. In their original form, they contain the likelihood of the data given the models. Unfortunately, in many applications, it is practically impossible to calculate the likelihood, and, therefore, the criteria have been reformulated in terms of descriptive statistics of the residual distribution: the variance and the mean-squared error of the residuals. These alternative versions are strictly valid only in the presence of additive noise of Gaussian distribution, not a completely satisfactory assumption in many applications in science and engineering. Moreover, the variance and the mean-squared error are quite crude statistics of the residual distributions. More sophisticated statistical indicators, capable of better quantifying how close the residual distribution is to the noise, can be profitably used. In particular, specific goodness of fit tests have been included in the expressions of the traditional criteria and have proved to be very effective in improving their discriminating capability. These improved performances have been demonstrated with a systematic series of simulations using synthetic data for various classes of functions and different noise statistics.