To find new pulsars, you obviously need to look at the sky with a big radio telescope. But once you've recorded this massive amount of data, you're only half done. Searching through the results, it turns out, is a massive computational task, even with modern computer clusters.
In principle, searching for pulsars in the data should be simple: they're nice periodic signals, so you just take a fast Fourier transform and the periodic signals show up as peaks in the power spectrum. In practice it's a lot more complicated than that.
The first problem you run into is interstellar dispersion. The space between stars is not empty: there is something like one hydrogen atom per cubic centimeter, on average. It is mostly hot, and at such a low density that means the hydrogen is in the form of plasma, loose electrons and protons. Plasma has a refractive index: it slows radio waves propagating through it very slightly. The absolute amount is small, maybe a few milliseconds, but it depends on the observing frequency. So if your pulsar has a one-millisecond pulse, then you might find that at the bottom of your observing band it's delayed by several milliseconds compared to the top of your observing band. This smears out a nice sharp pulse into a smeary blob that's much harder to detect. So when we look for pulsars, we need to divide the observing band into many channels and record the power in each channel separately. When you multiply a thousand channels by ten thousand power measurements a second, you get an awful lot of data.
To search for pulsars in this huge stream of data, you have to choose a dispersion measure (DM) and shift the channels accordingly, then add. Now you can do your Fourier transform and look for peaks.
The second problem that arises is acceleration. Some of the most interesting pulsars are not solitary stars. Instead, they are in orbit with a companion. Usually the pulsar is the more massive of the two, but it does not stand still; both stars orbit their common center of mass. Since these binary systems are often very close together and often have quite short periods - hours to days - the pulsar can be moving quite quickly, which produces a Doppler shift. A constant Doppler shift just changes the position of the peak, which is not a problem, but if the pulsar is accelerating - and moving in a circle involves acceleration - the Doppler shift can change. If the system is close enough, the Doppler shift can change substantially over the course of an observation, so that instead of the pulsar appearing at a single frequency in the Fourier transform, it gets spread out. Now, this may sound rare, and it is, but not only are these some of the most interesting systems to find, they are also the systems most likely to have been missed in previous surveys. So it's important to look for these accelerated systems, which you can do by looking not just for single peaks but for the characteristic shape of an accelerated peak. This is effective but takes substantial computing time.
Multiply together all the DM trials, Fourier frequencies, and possible accelerations, and you find that processing a single two-minute beam takes some thirty hours on a modern computer. Work out how many two-minute beams there are in the 1491 observing hours we carried out for the drift-scan survey, and you begin to see why we haven't yet processed all the data.
Once you've processed the data, though, you unfortunately do not get a nice tidy list of pulsars. There are rather a lot of processes that produce periodic radio signals, so what you get is a monster list of cell phone calls, car ignitions, telescope birdies, electric blankets, and a very few new pulsars. Winnowing through the huge mass of periodic signals is unfortunately less a question of clever Fourier techniques and more akin to spam filtering: many are obviously junk (they don't show interstellar dispersion, say), some are less-obvious junk, and a few are hard to tell from real pulsars even by humans.
My main contribution to the survey has been to run data through McGill's computer cluster and to sift through the results. I've done a certain amount of sifting by hand, but I've also written a few "spam filters" to try to reduce the number of obviously-not-pulsar results I have to look at. The real excitement, though, has been finding new pulsars and following them up.
Pizzagate and Kahneman, two great flavors etc.
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