*Article* **Two Classes of Gamma-ray Bursts Distinguished within the First Second of Their Prompt Emission**

**Lána Salmon \* , Lorraine Hanlon and Antonio Martin-Carrillo**

School of Physics and Centre for Space Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland; lorraine.hanlon@ucd.ie (L.H.); antonio.martin-carrillo@ucd.ie (A.M.-C.) **\*** Correspondence: lana.salmon@ucdconnect.ie

**Abstract:** Studies of Gamma-Ray Burst (GRB) properties, such as duration and spectral hardness, have found evidence for additional classes, beyond the short/hard and long/soft prototypes, using model-dependent methods. In this paper, a model-independent approach was used to analyse the gamma-ray light curves of large samples of GRBs detected by BATSE, *Swift*/BAT and *Fermi*/GBM. All the features were extracted from the GRB time profiles in four energy bands using the Stationary Wavelet Transform and Principal Component Analysis. t-distributed Stochastic Neighbourhood Embedding (t-SNE) visualisation of the features revealed two distinct groups of *Swift*/BAT bursts using the T<sup>100</sup> interval with 64 ms resolution data. When the same analysis was applied to 4 ms resolution data, two groups were seen to emerge within the first second (T<sup>1</sup> ) post-trigger. These two groups primarily consisted of short/hard (Group 1) and long/soft (Group 2) bursts, and were 95% consistent with the groups identified using the T<sup>100</sup> 64 ms resolution data. Kilonova candidates, arising from compact object mergers, were found to belong to Group 1, while those events with associated supernovae fell into Group 2. Differences in cumulative counts between the two groups in the first second, and in the minimum variability timescale, identifiable only with the 4 ms resolution data, may account for this result. Short GRBs have particular significance for multi-messenger science as a distinctive EM signature of a binary merger, which may be discovered by its gravitational wave emissions. Incorporating the T<sup>1</sup> interval into classification algorithms may support the rapid classification of GRBs, allowing for an improved prioritisation of targets for follow-up observations.

**Keywords:** gamma-ray burst; feature extraction; machine learning
