and eliminate them earlier in the data acquisition process. This would make our data set smaller and cleaner, which could increase the probability of finding an ETI signal. We track the birdies removed by Nebula and find that they come from the same DBSCAN group and are heavily polluted by ambient RFI. Figure \[fig:13\] shows the data before and after our pipeline. We are convinced our pipeline effectively removes most of the RFI and protects most of the birdies and potential candidates.

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Candidate selection
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With the clean data, we use DBSCAN to search for dense clusters and select clusters with the two rules above. The result is shown in Table \[tab:4\]. 19 groups of birdies and 83 groups of candidates are selected. We are very happy to note that our pipeline found 277 birdies, 94.2177$\%$ of the total. Only one group of birdies is removed by Nebula. This means that we can successfully find candidate targets that match our desired characteristics. All of the candidates found are shown in Figure \[fig:14\].

In order to identify whether the candidates we found are unremoved RFI, we examine them with all hits found in the raw data to see if 