To transform a set of time-based data into a set of frequency-based data, we apply a relatively complex mathematical operation called a "Fast Fourier Transform" or FFT.

The large graph in the lower frame of the SETI@home screensaver displays data resulting from FFT processing. At the beginning of a work-unit, we perform 15 different FFT's, each examining the data with varying resolution. We begin by looking for details as small as .07 Hz wide. There are tradeoffs with this type of analysis. If you want to be very accurate in frequency, you need to observe the data in longer chunks of time. For example, at the 0.075 Hz frequency resolution, we must look at chunks of data 13.42 seconds in length. To completely analyze our 107 second sample, we need to do 8 of these FFT's. When we reduce the frequency resolution to 0.14 Hz we only have to look at a 6.7 second sample of data. We now have less frequency resolution, but we have more time resolution. We have to look at twice the number of these (16 of them) to cover our 107 seconds of data! We look at 15 different frequency resolutions (0.075, 0.15, 0.3, 0.6, 1.2, 2.5, 5, 10, 20, 40, 75, 150, 300, 600, and 1200 Hz) in our analysis. With each halving of the frequency resolution we must perform twice the number of FFT's to cover 107 seconds of work-unit data. The amount of number crunching necessary is impressive.