Traditionally, gamma-ray bursts (GRBs) are classified as long and short GRBs, with T 90 = 2 s being the threshold duration. Generally, long-duration GRBs (LGRBs; T 90 > 2 s) are associated with the collapse of massive stars, and short-duration (SGRBs; T 90 < 2 s) are associated with compact binary mergers involving at least one neutron star. However, the existence of a population of so-called “peculiar GRBs”—i.e., LGRBs originating from mergers or long Type I GRBs, and SGRBs originating from collapsars or short Type II GRBs—has challenged the traditional paradigm of GRB classification. Finding more peculiar GRBs may help to give more insight into this issue. In this work, we analyze the properties of machine-learning-identified long Type I GRB and short Type II GRB candidates, long GRBs-I and short GRBs-II (the so-called “peculiar GRBs”). We find that long GRBs-I almost always exhibit properties similar to Type I GRBs, which suggests that mergers may indeed produce GRBs with T 90 > 2 s. Furthermore, according to the probability given by the redshift distribution, short GRBs-II almost exhibit properties similar to Type II GRBs. This suggests that the populations of short Type II GRBs are not scarce and that they are hidden in a large number of samples without redshifts, which is unfavorable for the interpretation that the jet progression leads to a missed main emission.
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