# Performance Comparison of Machine Learning Disruption Predictors at JET
## 2 Materials and Methods
### Database  
The data from this study come from a database, created and maintained by the University of Cagliari, containing hundreds of disrupted and regularly terminated discharges from some of the JET experimental campaigns performed after the installation of the ITER-Like Wall (ILW) [9; 33], from 2011 to 2020. Only the discharges with a flat-top plasma current higher than 1.5 MA, and a flat-top length greater than 200 ms, where all the diagnostics described in Table 1 were available and consistent, have been selected. Moreover, the discharges caused by a vertical displacement event and those in limiter configurations were excluded. Moreover, only disruptions occurring at the flat-top phase have been considered here. In total, the database for this work contains 193 disrupted and 219 regularly terminated discharges, which is the same as in [33]. The flat-top starting time has been assumed as the first time instant where the plasma is in an X-point configuration. Both 0-D and 1-D diagnostic signals have been collected to extract the feature vector \(x\) and are reported in Table 1. The literature demonstrated the benefit impact of the recent introduction of these 1-D plasma profiles [8; 9; 28; 30; 33; 36; 37]. The temperature and density profiles come from high-resolution Thompson scattering (HRTS), the radiated power profile comes from the horizontal lines of sight of the bolometer, the internal inductance comes from the code BetaLi for the estimation of the poloidal beta and internal inductance, while the locked mode amplitude comes from the saddle loops (LMS) and is normalized by the plasma current.  
These diagnostic signals are, usually, preprocessed (data-reduced, filtered, sampled, normalized, and windowed) and a feature engineering process can follow to extract the features more suitable to the chosen DP architecture from the raw signals.  
Table 2 reports the number of discharges and the originating campaigns for the training, validation, and test sets. Note that, as highlighted in [33], the training set contains experiments carried out in the early operation phase of the JET-ILW, and the operational space of the JET has changed in the following experimental campaigns. In fact, more recent JET-ILW experiments are run at a higher power and plasma current, causing higher disruption rates. This change may affect the disruption patterns with respect to those observed in the training set discharges [24; 38]. This makes such test sets particularly challenging to evaluate the robustness of the disruption predictors at the change of the operating conditions as well as the aging effect, inherent in any data-driven predictor, and to estimate the suitability of the selected set of diagnostics. The comparison of the three ML DP models has been performed referring to the same diagnostic signals and the same training, validation, and test pulses reported in Table 2. Note that this is the number of experiments included in the dataset; each experiment consists of several multidimensional samples sampled with a high frequency (2 ms). Considering the training and validation data, the dataset is composed of more than 100,000 multidimensional samples, a sufficient number to train the models.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Plasma Signal** & **Acronym** & **Diagnostics** & **Dimension** \\ \hline Electron temperature & \(T_{e}\) & HRTS & 1-D \\ Electron density & \(n_{e}\) & HRTS & 1-D \\ Radiated power & \(P_{rad}\) & Bolometer & 1-D \\ Total radiated power & \(P_{rad-TOT}\) & Bolometer & 0-D \\ Total input power & \(P_{TOT}\) & BetaLi & 0-D \\ Internal inductance & \(l_{i}\) & BetaLi & 0-D \\ Normalized locked mode & \(LM_{norm}\) & LMS & 0-D \\ \hline \hline \end{tabular}
\end{table}
Table 1: Diagnostic signals, acronyms, and units.