  Classification of gamma-ray bursts (GRBs) has been a long-standing puzzle in
high-energy astrophysics. Recent observations challenge the traditional short
vs. long viewpoint, where long GRBs are thought to originate from the collapse
of massive stars and short GRBs from compact binary mergers. Machine learning
(ML) algorithms have been instrumental in addressing this problem, revealing
five distinct GRB groups within the Swift/BAT light curve data, two of which
are associated with kilonovae (KNe). We corroborate these five classes by
extending this analysis to the Fermi/GBM data using unsupervised ML techniques.
These five clusters are well separated in fluence-duration plane, hinting at a
potential link between fluence, duration and complexities (or structures) in
the light curves of GRBs. Further, we confirm two distinct classes of
KN-associated GRBs. The presence of GRB 170817A in one of the two
KNe-associated clusters lends evidence to the hypothesis that this class of
GRBs could potentially be produced by binary neutron star (BNS) mergers. The
second KN-associated GRB cluster could potentially originate from NS-BH
mergers. Future multimessenger observations of compact binaries in
gravitational waves (GWs) and electromagnetic waves can be paramount in
understanding these clusters better.
