================ Clustering ================ The clustering step applies the DBSCAN algorithm (see `Men and Barr (2024) `_) to group detected single pulse events into candidates based on their proximity in time and DM space. This helps reduce duplicate detections. The options ``-r`` and ``-k`` set the parameters ``eps`` and ``min_samples`` of the DBSCAN algorithm, which control clustering sensitivity. A larger ``eps`` value allows more distant points to be considered part of the same cluster, while a larger ``min_samples`` value requires more points to form a cluster. The option ``--maxncand`` limits the maximum number of candidates generated after clustering one data chunk. If the number of candidates exceeds this limit, only the top candidates with the highest S/N are kept for further processing. The option ``--drop`` drops candidates whose boxcar width equals the maximum boxcar width used in matched filtering. These candidates are likely RFI, and dropping them helps reduce false positives. .. image:: ../images/clustering_diagram.png :width: 600px :align: center