The July 31 issue of IEEE Spectrum includes an article on the search for a ninth planet (now that Pluto has been demoted) lurking on the dark outer fringes of our solar system. University of California, Berkeley graduate students Michael Medford and Danny Goldstein are joining the hunt using the machine-learning pipeline and infrastructure created for the Palomar Transient Factory effort headed up by CRD’s Peter Nugent. An excerpt follows:
Drawing on hundreds of thousands of images covering the search area for Planet Nine—all shot from 2009 to 2016 using a 1.2-meter telescope in the mountains north of San Diego—their system will combine multiple images in an ingenious way that should brighten the faint flickers of light from Planet Nine enough to distinguish them from background noise.
“Because the planet is moving with respect to the background stars, you can’t just add overlapping images together,” Medford points out. Instead, their software selects each of the many distinct plausible orbits for Planet Nine, projects the planet’s movement onto the relevant patch of sky, and then offsets successive images to superimpose—and brighten—any pixels corresponding to the planet. A pipeline of software written with Peter Nugent, their faculty advisor, performs the overlapping and subtracts known objects such as stars.
The computational task is enormous because the planet’s orbit is still so uncertain. To do a 98 percent complete search, Medford estimates, they will need to perform 10 billion image comparisons. Fortunately, Nugent has time allocated on the Cori supercomputer, a new Cray XC40 system that recently ranked as the fifth most powerful in the world.
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