## 5 Conclusions  
This work presents a new methodology to evaluate the efficiency of ELM pacing by pellets. The type of analysis is \"by pulse\", so it does not require a reference pulse to be performed. It combines the spectrogram cross-correlation technique with the unsupervised k-means approach to statistically assess: (a) if during a plasma pulse there are triggered ELMs or not, (b) which ELM was triggered, (c) which pellet actually triggered an ELM, (d) the triggering efficiency in both terms of percentage of triggered ELMs and percentage of triggering pellets, and (e) the triggering time delay. The analysis of the spectrogram cross-correlation signals of each triggered ELM indeed allows to calculate the triggering time distribution, i.e., the time delay between a triggering pellet and the triggered ELM in terms of rising edges of the signals. The results are in line with the ones calculated by the numerical simulations [26], even in very different plasma scenarios, and confirm the importance of the pellets pacing technique to trigger ELMs, as demonstrated empirically at JET [27; 43]. Furthermore, the statistical results obtained in this contribution confirm the efficiency of the pellets technique for pacing ELMs. The suggested methodology is expected to have several outcomes in ELM studies. In fact, the capability of classifying which ELM has been triggered and which pellet triggered an ELM is a precious piece of information to tune the pacing experiment, i.e., to optimize the ELM pacing. However, further analysis is suggested to improve the performances, that in this study reach, in any case, the upper limit of 73%. The methodology shown here can be in fact be used to select  
Figure 12: Average triggering time using diamond sensor and beryllium spectroscopy. The black line reports the expected triggering time window with JOREK [17]. As it can be seen, even in different physical scenarios, the estimates are compatible, but the diamond UV detector’s ones have averages more in line with numerical estimates. the not-triggered ELMs and consequently, to consider in more detail in future studies, the physical causes of their behaviour. The paper also highlights the importance of the UV diamond sensor as a possible diagnostic for ELM tracking. The results obtained with this diagnostic are comparable to the Be spectroscopy one. Indeed, both found the same triggered ELMs, the same triggering efficiency, the same triggering pellets, and comparable triggering times within the estimated uncertainties. However, the diamond detector provides estimates more in line with the expectation [26] as Figure 12 reports, even with very different plasma conditions, as can be expected by considering the different physical phenomena occurring at the edge in the main region and those affecting the spectroscopic signal in the divertor. In particular, the average SPCC signals of the triggered ELM are more peaked in the case of a diamond sensor, as Figure 10 shows, while the signal in the case of the Be line, is characterised by both a wider peak and a second local maximum. This feature implies that the calculation of the triggering time is less accurate for the Be-based signal, as confirmed by the smaller standard deviation of the triggering time calculated with the diamond sensor. All these elements suggest that diamond sensors are good candidates for ELM detection and analysis. Despite the results obtained, it is also worth underlining that the line of sight and the signal amplification of the diamond sensor is not optimised for looking at ELMs. Conceptualization, R.R. and E.P. ; data curation, F.B., P.G., M.G., M.M. and G.V.R. ; formal analysis, R.R., S.C. and E.P. ; funding acquisition, P.G. and M.M. ; investigation, R.R., S.C., F.B., P.G., M.G., M.M., G.V.R. and E.P. ; methodology, R.R. and E.P. ; project administration, P.G. and M.M. ; software, R.R. ; supervision, E.P. ; visualization, R.R. and S.C.; writing--original draft, R.R. and E.P. ; writing--review and editing, R.R., S.C., F.B., P.G., M.G., M.M., G.V.R. and E.P.