Radio Astronomers Are Increasingly Using Convolutional Neural Networks To Sift Through Massive Amounts of Data (

Radio astronomers have so far cataloged fewer than 300 fast radio bursts, mysterious broadband radio signals that originate from well beyond the Milky Way. Almost a third of them — 72, to be precise — were not detected by astronomers at all but instead were recently discovered by an artificial intelligence (AI) program trained to spot their telltale signals, even hidden underneath noisy background data. The very first recorded fast radio burst, or FRB, was spotted by radio astronomers in 2007, nestled in data from 2001, reads a report on IEEE Spectrum. Today, algorithms spot FRBs by sifting through massive amounts of data as it comes in. However, today’s best algorithms still can’t detect every FRB that reaches Earth. That’s why AI developed by Breakthrough Listen, a SETI project headed by the University of California, Berkeley, which has already found dozens of new bursts in its trial run, will be a big help in future searches. The report adds: There are a few theories about what FRBs (fast radio bursts) might be. The prevailing theory is that they’re created by rapidly rotating neutron stars. In other theories, they emanate from supermassive black holes. Even more out-there theories describe how they’re produced when neutron stars collide with stars composed of hypothetical dark matter particles called axions. The bursts are probably not sent by aliens, but that theory has its supporters, too. What we do know is that FRBs come from deep space and each burst lasts for only a few milliseconds. Traditionally, algorithms tease them out of the data by identifying the quadratic signals associated with FRBs. But these signals are coming from far-flung galaxies. “Because these pulses travel so far, there are plenty of complications en route,” says Zhang. Pulses can be distorted and warped along the way. And even when one reaches Earth, our own noisy planet can obfuscate a pulse. That’s why it makes sense to train an AI — specifically, a convolutional neural network — to poke through the data and find the ones that traditional algorithms missed. “In radio astronomy,” says Zhang, “at least nowadays, it’s characterized by big data.” Case in point: The 72 FRBs identified by the Berkeley team’s AI were found in 8 terabytes of data gathered by the Green Bank Telescope in West Virginia. To even give the AI enough information to learn how to spot those signals in the first place, Zhang says the team generated about 100,000 fake FRB pulses. The simple quadratic structure of FRBs makes it fairly easy to construct fake pulses for training, according to Zhang. Then, they disguised these signals among the Green Bank Telescope data. As the team explains in their paper [PDF], accepted by The Astrophysical Journal with a preprint available on arXiv, it took 20 hours to train the AI with those fake pulses using a Nvidia Titan Xp GPU. By the end, the AI could detect 88 percent of the fake test signals. Furthermore, 98 percent of the identifications that the AI made were actually planted signals, as opposed to the machine mistakenly identifying background noise as an FRB pulse.

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