to test the percentage of simulated “birdie” signals lost due to RFI removal at different threshold values. This can be seen in Figure \[fig:21\]. The threshold value of 90% conserved the most birdies, which maximizes the probability of conserving true ETI signals while minimizing the unremoved RFI. See Section \[sec:birdies\] for more discussion of birdie generation.

Figure \[fig:6\] is a waterfall plot before and after using machine learning to remove RFI. Figure \[fig:8\] is an example of removing broad-band RFI. The KNN algorithm can effectively remove much of the broad-band and narrow-band RFI left by Nebula. Following this removal the data are clean enough for selecting candidates.

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Machine learning for candidate selection
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After RFI removal, we apply a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [@Ester:1996:DAD:3001460.3001507], a density clustering algorithm, to find candidate clusters.

DBSCAN is a clustering algorithm based upon data density. It separates the data into three kinds of points: “core points," “adjacent points," and “noisy points." “Core points" are those with more than a predefined number of points within a specified radius, while “adjacent points” have fewer points nearby but belong to a core point. “Noisy points” neither have enough points around nor 