*5.1. Swift/BAT*

For *Swift*/BAT, the three components identified in Figure 1b had a similar size and structure to those identified in the GMM clustering by Bhave et al. [54]. In this analysis, the hardness ratio was computed using the best-fit model for *Swift*, consistent with the method undertaken by Bhave et al. [54], enabling comparison of results. The clear-cut round boundary between the intermediate and long components in Figure 1b was also found by Bhave et al. [54] and is a signature of the application of a Gaussian model to a non-Gaussian underlying distribution.

The result of applying clustCombi after the GMM clustering indicated that the intermediate duration component, combined with the long duration component, provided a better fit to the sample of *Swift*/BAT bursts (Figure 3). Thus, the intermediate class was likely identified by the overfitting resulting from GMM clustering applied to the complex distribution of *Swift*/BAT bursts in duration-hardness space.

Figure 4 and Table 3 show that the mean duration of the short class identified by clustCombi was T<sup>90</sup> ≈ 0.3 s (1 *σ* standard deviation of 0.29 s), while the long class had a mean T<sup>90</sup> ≈ 70 s (1 *σ* standard deviation of 101 s). This is consistent with the peaks of the *Swift* short (T<sup>90</sup> < 2 s) and long (T<sup>90</sup> > 2 s) duration distributions [85]. The shorter duration class had a larger hardness ratio than the longer duration class, as expected from the traditional short/long paradigm. The separation between the short and long classes occurred at T<sup>90</sup> ≈ 0.5–2 s. This is in agreement with the findings of Bromberg et al. [86], whose modelling of the duration distribution of *Swift*/BAT bursts using the Collapsar model suggested a separation at T<sup>90</sup> ≈ 0.8 s.
