Scientists have discovered 8 possible alien “technosignatures” coming from nearby stars

They found eight extraterrestrial signals that appear to have signs of technology. (Credit: Getty Images)

Are we alone in the universe?

Perhaps scientists have just brought us closer to answering this question. A team led by researchers at the University of Toronto has made the search for extraterrestrial life easier by using a new algorithm to organize data from their telescopes into categories to distinguish between real signals and interference. This allowed them to quickly sort information and find patterns using an artificial intelligence process known as machine learning.

They found eight extraterrestrial signals that appear to have signs of technology. The study, published in the journal Nature Astronomy, does not claim to have found evidence of intelligent aliens, but researchers believe that using artificial intelligence is a promising way to search for extraterrestrial intelligence.

“I am impressed with how well this approach has performed in the search for extraterrestrial intelligence,” study co-author. Cherry Ngsaid an astronomer from the University of Toronto. statement. “I am confident that with the help of artificial intelligence, we will be able to better quantify the likelihood of the presence of extraterrestrial signals from other civilizations.”

The Search for Extraterrestrial Intelligence, or SETI, has been ongoing since the 1960s and has focused on finding evidence of technologically generated signals, known as technosignatures, from advanced extraterrestrial civilizations. Astronomers have used powerful radio telescopes to scan thousands of stars and hundreds of galaxies in the hope of finding these technosignatures. It is assumed that an advanced extraterrestrial civilization will have sufficient complexity to emit such signals.

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Despite being located in areas with minimal technology interference, the search for extraterrestrial intelligence (SETI) still faces significant challenges due to human interference. Peter Ma, an undergraduate student and researcher at the University of Toronto, explains that “a lot of our observations have a lot of interference.”

To distinguish extraterrestrial signals from human interference, the team trained their machine learning tools by simulating both types of signals. They tested many algorithms, evaluated their accuracy and false positive rate, and ultimately chose the powerful algorithm created by Ma.

The new methodology uses a method called “semi-supervised learning”, which combines supervised and unsupervised learning. The algorithm was first trained to distinguish between man-made radio signals originating from Earth and signals from other places. The researchers analyzed 150 terabytes of data from the Green Bank Telescope in West Virginia, covering observations of 820 stars near Earth, and found eight previously missed signals from five stars located between 30 and 90 light-years from Earth.

An artist’s rendering of the Green Bank Telescope connected to a machine learning network. (Source: Breakthrough Listen/Daniel Futselaar)

Ma’s algorithm, referred to as “semi-supervised learning”, is a combination of two subtypes of machine learning, supervised and unsupervised learning. It uses the strengths of both methods to improve the accuracy of the algorithm. In this approach, supervised learning is used to guide and train the algorithm, while unsupervised learning is used to discover hidden patterns in the data. This combination allows the algorithm to generalize the information received and more easily detect new patterns in the data, leading to better results when searching for extraterrestrial signals.

Ma’s pioneering idea to apply semi-supervised learning to SETI started as a school project. “I only told my team after the paper was published that it all started as a school project that my teachers didn’t really appreciate.”

Dr. Ng says that new ideas are very important in a field like SETI. “Having checked the data with all methods, we could find interesting signals.”

U of T student and researcher Peter Ma. (Photo: Polina Teif)

Breakthrough Listen SETI scientists say these signals have two things in common with signals that could be generated by intelligent aliens: they are present when looking at a star and absent when looking away, and their frequency changes over time in a way that which makes them appear far from the telescope. However, these features may arise by chance, and further observations are needed to make any claims about extraterrestrial life.

“First, they are present when we look at a star and absent when we look away—as opposed to clutter, which is usually always present,” Steve Croft (opens in new tab), Breakthrough Listen at Project Scientist the Green Bank. Telescope,” the message says. “Secondly, the frequency of the signals changes over time in such a way that they appear far from the telescope.”

Green Bank Telescope. (Source: Chris Schodt/Breakthrough Listen)

The research team hopes to apply their algorithm to data from more powerful radio telescopes such as South Africa’s MeerKAT or the planned Next Generation Very Large Array. They believe this new technique, combined with next-generation telescopes, will allow them to search for millions of stars rather than hundreds.

“With our new technique combined with next-generation telescopes, we hope that machine learning will help us move from searching for hundreds of stars to searching for millions,” Ma said.

Even though the initial results did not lead to the discovery of extraterrestrial life, the use of machine learning in the search for extraterrestrial intelligence holds great promise. The authors of the study are optimistic that artificial intelligence will help them better quantify the likelihood of the presence of extraterrestrial signals from other civilizations.

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