tokens: the DTW template matching scheme and a traditional peak detection algorithm. Curves are generated by varying the respective threshold parameter, DTW distance, and the peak prominence. 4 RESULTS Some of the detection scenarios involving true detection of long GRBs along with typical false-positive candidates are shown in Fig. 6 . The upper panels show the correctly identified events, while the lower panels depicted certain instances when the events picked up were due to noise artefacts. After the testing and validation steps mentioned in the previous section, we carried out a blind search targeting long GRB events on the CZTI data collected from 2015 October 8 to 2018 November 7. The pipeline detected 223 probable candidates, out of which 170 were already known to be GRBs. Detailed analysis of the rest led to the discovery of 35 long GRBs candidate events along with 18 false positives. Table 1 lists these newly discovered events along with their trigger time in UTC, T 90 , 2 peak count rate, total count rate, mean background counts, and detection significance with their uncertainties. Of these, GCN alerts were issued for two such events, namely GRB180526A (Sharma, Vibhute Bhattacharya 2018a ) and GRB180603A (Sharma, Vibhute Bhattacharya 2018b ). The observed number of false triggers are higher than the expected as shown in Fig. 5 . While the origin of many of these is not well understood, some of them seem to be related to the scenario shown in Fig. 6 (lower right), where we see multiple short-duration transients all occurring within tens of seconds. Figure 6. Few detection scenarios encountered while searching for long GRBs with one-second binning. The upper panel shows the correctly identified events, while the lower panel highlights instances of contamination from non-GRB transients. The horizontal red line determines the threshold caused by the noise background level, and significant points above this are clustered using the DBSCAN algorithm. Each cluster is uniquely coloured, with the vertical line providing the cluster centre. Table 1. GRBs candidate events detected with the machine learning algorithm described in this paper. GCN circulars have been issued for the highlighted events. Certain minor modifications were necessary to make the pipeline sensitive to GRBs occurring at shorter time-scales. As these events showed a higher sensitivity to the time bins’ size, it was necessary to vary the bin size across time-scales ranging from 32 to 512 ms for the entire data chunk and select the value that maximizes the peak count in at least three of the four quadrants. The time bin’s optimal value was determined using the differential evolution (Storn 1996 ) global optimizer, which performed a gradient-free direct search across this continuous parameter space. Besides, the 3σ detection threshold used for longer GRBs had to be increased to 4.5σ to minimize contamination from the background noise. In Fig. 7 we report the triggers seen for
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