**3. Feature Extraction**

The feature extraction algorithm consists of multiple steps, which are outlined in Figure 1 for the analysis of light curves in the T<sup>100</sup> interval at 64 ms resolution. Light curves were first pre-processed, before Stationary Wavelet Transform was applied. PCA was used to reduce the dimensionality of the resulting coefficients before visualisation with a t-SNE map. Figure 2 depicts the steps that were followed for the analysis of the first second of prompt emission. This section outlines the details of each step in the feature extraction algorithm.

**Figure 1.** Flowchart of the feature extraction and clustering algorithm for analysis of 64 ms-binned light curves in the interval T<sup>0</sup> to T100.

**Figure 2.** Flowchart of the feature extraction and clustering algorithm for analysis of light curves in the interval T<sup>0</sup> to T<sup>0</sup> + 1.004 s.
