Turn a radio telescope to the stars in the sky, and it’s instantly deafened. From pulsars to radio galaxies, and ionospheric disturbances in the atmosphere to radio-frequency interference (RFI) from our own technology, the sky is a cacophony of radio noise. And somewhere, among all that, may lie a needle in a haystack: a signal from another world.
For over 60 years scientists have been scanning the skies in the search for extraterrestrial life but have yet to find any aliens. When you consider the sheer volume of search space — all those stars, all those radio frequencies — versus our limited searches so far, then it’s little wonder we’ve not found ET yet. It’s a daunting task, especially for a human. Thankfully, we’ve got some non-human intelligence to join the search.
The use of artificial intelligence (AI) is reaching critical mass, in our everyday lives and in science, so it is no surprise that it’s now being employed in Search for Extraterrestrial Intelligence (SETI). AI is already helping astronomers make incredible discoveries. Here’s how. We’re not talking about Skynet, or the machines from The Matrix movies, or even Star Trek: The Next Generation’s Data. The AI that is so in vogue at present is based on machine-learning algorithms designed to do very specific jobs, even if it’s just to talk to you on ChatGPT.
To explain how AI is assisting in SETI, astronomer and SETI researcher Eamonn Kerins of the University of Manchester compares it to the needle in a haystack problem. “You basically treat the data as though it’s the hay,” Kerins told Space.com Space.com. “Then you’re asking the machine-learning algorithm to tell you if there is anything in the data that isn’t hay, and that hopefully is the needle in the haystack — unless there’s other stuff in the haystack too.”
That other stuff is usually RFI, but the machine-learning algorithm is trained to recognize all the types of RFI we already know about. Those signals — the familiar patterns of mobile phones, local radio transmitters, electronics and so on — are the hay. The training involves “injecting signals into the data and then the algorithm learns to look for signals that are like that,” Steve Croft, an astronomer with the Breakthrough Listen SETI project at the University of California, Berkeley, told Space.com The algorithm learns to spot the patterns of these familiar signals and disregard them. Should it spot something in the data that it hasn’t been trained on, then it flags this up as something interesting that requires a human to follow up on.
“There have been attempts recently at sifting through some of the Breakthrough Listen data with a machine-learning algorithm,” said Kerins. “The data had already been combed through quite carefully previously by more conventional means, but yet the algorithm was still able to pick out new signals after being trained on the stuff that we know about.”
This project was led by Croft and an undergraduate student, Peter Ma of the University of Toronto, who wrote the algorithm and put it to work analyzing data from 820 stars observed by the 100-meter radio telescope at Green Bank Observatory in West Virginia. The data, totaling 489 hours’ worth of observations, contained millions of radio signals, almost all of which were human-made interference. The algorithm checked every single one of them and found eight signals that did not match anything it had been trained on and which had been missed by earlier analyses of the data.
Read the full article at: www.space.com