are still some atypical RFI left. Two examples are narrow-band RFI and broad-band RFI. We are unable to detect all narrow-band RFI, because at times the power is below our threshold. It is easy to find broad-band RFI if the bandwidth is very large. If the bandwidth is less than several MHz, it is much more difficult and our traditional methods do not detect them.

Of course, these RFI signals still have characteristics in common: such emissions cluster in time and frequency on specific scales. The ETI signals typically would not form a large cluster on time and frequency scales, being narrow in frequency and of no longer duration than the telescope observed a point in the sky in the drift scan. We use the k-Nearest-Neighbor(KNN) algorithm to find the nearest 100 hits for each hit and calculate the mean distance, as was first applied by @vishala. Figure \[fig:5\] plots the histogram of hits’ mean distances for the 100 nearest hits. The blue line is the upper RFI threshold, which is based on removing a specified percentage (90$\%$) of the total hits. Hits below this threshold are presumed to be RFI events. We tested our threshold choice using experiments conducted 