With better computer systems comes more battery imagery. Or at least thats true the majority of the time. Supercomputers are extraordinarily proficient at image processing, so its usually worth it when a new algorithm comes along that they can turn their attention to. Thats exactly what happened with an algorithm recently developed by Ph.D. trainee Frits Sweijen and his colleagues at Leiden University. They utilized a number of supercomputers image processing power to mimic and boost the resolution of radio images caught by the International LOFAR telescope.. Mr. Sweijen and his team utilized their algorithm to fix an especially thorny issue in radio astronomy– how to filter out sound from our atmosphere. The sun charges the Earths environment with particles that hinder radio signals, making it challenging to catch a clear image of the radio-emitting phenomena with ground-based radio telescopes..
Some of the initial radio images of galaxies (left) with their high-resolution equivalents (right). Thats where the algorithm comes in.
They utilized the Academic Leiden Interdisciplinary Cluster Environment (ALICE) and Spider supercomputing platforms to parallelize the estimations that resulted in much greater resolution images that had previously been readily available. The resolution was so high, in fact, that one section, which was a location about the size of a moon, would have as many pixels in it as a normal radio map of the complete sky.
The LOFAR superterp core station in Exloo, the Netherlands. Credit: ASTRON/LOFAR.
Realistically, that is not that much time. With battery imagery also comes better science.
Discover more: Leiden University– A much sharper image of deep space with new algorithms and supercomputersLeiden University– Ultra-sensitive radio images expose thousands of star-forming galaxies in early UniverseSpace.com– Epic radio images offer most-detailed views of remote galaxiesUT– LOFAR Sees Strange Radio Signals Hinting at Hidden Exoplanets.
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Some of the original radio images of galaxies (left) with their high-resolution equivalents (right). Thats where the algorithm comes in. To do this needed a lot of processing power, for which the team turned to supercomputers.
With battery images also comes better science.