data in post processing. We cross checked our approximate calculated positions with the more accurate positions provided to us later by the FAST telescope pointing system – our position errors are under 0.014 degree, which is adequate for testing the Nebula data analysis pipeline, although not ideal for the upcoming sky survey. We expect the pointing errors in the upcoming sky survey will be significantly lower, as our SETI spectrometer will have access to accurate real time telescope pointing data, and the spectrometer will merge this pointing data with the science data before it’s recorded to disk.**]{}

Machine learning for RFI removal and candidate selection {#sec:5}
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It should be noted that we employ the traditional assumption that advanced life wishing to be detected at interstellar distances will use narrow-band microwave emissions as narrow-band signals are most easily distinguished from emissions produced by natural astrophysical sources of microwave emission. The narrowest known natural sources, astrophysical masers, have minimum frequency widths of about 500 Hz [@1987MNRAS.225..491C]. Thus we primarily focus on searches for narrow-band signals from ETI.

Machine learning for RFI removal
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Our Nebula pipeline can remove most of the RFI. Normally, $>99\%$ of RFI can be removed, but there 