Alexandra Witze

From the hills of West Virginia to the flats of rural Australia, some of the world’s largest telescopes are listening for signals from distant alien civilizations. The search for extraterrestrial intelligence, known as SETI, is an effort to find artificial-looking electromagnetic-radiation signals that might have come from a technologically advanced civilization in a far-away solar system. A study published today1 describes one of several efforts to use machine learning, a subset of artificial intelligence (AI), to help astronomers sift quickly through the reams of data such surveys yield. As AI reshapes many scientific fields, what promise does it hold for the search for life beyond Earth?

“It is a new era for SETI research that is opening up thanks to machine-learning technology,” says Franck Marchis, a planetary astronomer at the SETI Institute in Mountain View, California.

The problem of big data is relatively new for SETI. For decades, the field was constrained by having hardly any data at all. Astronomer Frank Drake pioneered SETI in 1960, when he pointed a telescope in Green Bank, West Virginia, towards two stars and listened for radio transmissions. Most of the SETI searches that followed were also limited to a small number of stars.

But in 2015, billionaire Yuri Milner funded the biggest SETI program ever, in Berkeley, California: the Breakthrough Listen project to search one million stars for signs of intelligent life. Using telescopes in West Virginia, Australia and South Africa, the project looks for radio emissions that come from the direction of a star and that change steadily in frequency, as would happen if an alien transmitter were on a planet moving with respect to Earth.

Data blizzard

The trouble is that these searches yield a blizzard of data — including false positives produced by Earthly interference from mobile phones, GPS and other aspects of modern life.

“The biggest challenge for us in looking for SETI signals is not at this point getting the data,” says Sofia Sheikh, an astronomer at the SETI Institute. “The difficult part is differentiating signals from human or Earth technology from the kind of signals we’d be looking for from technology somewhere else out in the Galaxy.”

Going through millions of observations manually isn’t practical. A common alternative approach is to use algorithms that look for signals matching what astronomers think alien beacons could look like. But those algorithms can overlook potentially interesting signals that are slightly different from what astronomers are expecting.

Enter machine learning. Machine-learning algorithms are trained on large amounts of data and can learn to recognize features that are characteristic of Earthly interference, making them very good at filtering out the noise.

Overlooked signals

Machine learning is also good at picking up candidate extraterrestrial signals that don’t fall into conventional categories and so might have been missed by earlier methods, says Dan Werthimer, a SETI scientist at the University of California, Berkeley.

Peter Ma, a mathematician and physicist at the University of Toronto, Canada, and lead author of today’s paper, agrees. “We can’t always be anticipating what ET might send to us,” he says.

Ma and his colleagues sifted through Breakthrough Listen observations of 820 stars, made using the 100-metre Robert C. Byrd Green Bank Telescope. They built machine-learning software to analyze the data; this netted nearly three million signals of interest but discarded most as Earth-based interference. Ma then manually reviewed more than 20,000 signals and narrowed them down to 8 intriguing candidates.

The search ultimately came up empty — all eight signals disappeared when the team listened again. But the methods could be used on other data, such as a flood of observations from the MeerKAT array of 64 radio telescopes in South Africa, which Breakthrough Listen began using in December. The machine-learning algorithms could also be used on archived SETI data, says Ma, to seek signals that might previously have been overlooked.

Citizen SETI

Machine learning is also at the heart of a separate SETI effort that will launch next month. On 14 February, astronomers at the University of California, Los Angeles (UCLA), will launch a community-science project in which volunteers from the public will sort through images of radio signals and classify them as potential types of interference, to train a machine-learning algorithm to search SETI data from Green Bank.

And AI can help with other stages of the SETI process. Werthimer and his colleagues have used machine learning to come up with a ranking of stars to be observed in an ongoing SETI project that uses the world’s largest single-dish telescope, the 500-metre FAST radio telescope in China.

Still, SETI will probably continue to use a mixture of classical and machine-learning approaches to sort through data, says Jean-Luc Margot, an astronomer at UCLA. Classical algorithms remain excellent at picking up candidate signals, and machine learning is “not a panacea”, he says.

___________________

Courtesy: Nature (Posted on Jan 30, 2023)

The post Artificial Intelligence could spot alien signals humans miss first appeared on Sindh Courier.

By Alexandra Witze

Astronomers have pinpointed more than 2,000 stars from where, in the not-too-distant past or future Earth can occasionally be detected transiting across the face of the Sun.

If there are aliens living on planets around those stars, with at least a similar level of technological advancement to our own species, then they would theoretically be able to spot us. They could even have observed as the amount of carbon dioxide in Earth’s atmosphere increased over the past several hundred years, since the industrial revolution.

The work, reported in this week’s Nature, offers a new way of thinking about the search for extraterrestrial life, says Lisa Kaltenegger, an astronomer at Cornell University in Ithaca, New York, who led the analysis. “Who has the cosmic front seat to see us?” she asks. “For whom would we be the aliens?”

Those aliens would be the natural choice for Earthlings to look for, say the scientists — because they may have already had a chance to spot us, and thus might be primed to be ready for communications from Earth.

Movement of stars over time

Although previous studies have considered this question this is the first to incorporate the movement of stars over time, because stars can slide in or out of the narrow slice of the sky that happens to line up with both Earth and the Sun. With this information, the scientists were able to predict where Earth was visible from over the past 5,000 years or so of human civilization — and also predict where it will be visible another 5,000 years into the future.

In doing so, the study expands astronomers’ thinking about which stars have “a better-than-average shot of discovering and characterizing the Earth,” says Sofia Sheikh, an astrobiologist at the Berkeley SETI Research Center in California.

The discovery was made possible by the European Space Agency’s Gaia space observatory, which has compiled the best three-dimensional map of stars to date. Working with Jackie Faherty, an astronomer at the American Museum of Natural History in New York City, Kaltenegger analysed the Gaia map to see which stars have been, or will be, in a position where Earth briefly moves between them and our Sun. Because most of the sky lies in other planes to that of our Solar System, there’s just a tiny sliver where this is possible, she says. Of the more than 330,000 stars in the Gaia catalogue that are within 100 parsecs of Earth, just 2,043 happen to have the perfect viewing geometry.

Of those, 1,715 are in the right locations to have spotted Earth in the past 5,000 years, and an additional 319 will have vantage points in the next 5,000 years (see ‘All eyes on Earth’). Seven of the 2,034 are already known to host planets — but many more are likely to have worlds orbiting them, some of which may be suitable for life.

The method assumed for spying Earth from elsewhere in the Galaxy is the same one that Earth-bound astronomers have used to discover thousands of exoplanets: detecting the light of a distant star dimming slightly and regularly, as an orbiting planet passes across its face.

Good alien targets

With the results of this study, astronomers searching for extraterrestrial life can now focus on stars and planetary systems that have a view of Earth and thus might already expect to hear from us. “It really helps in the hunting if you know where the prey is located,” says Seth Shostak, an astronomer at the SETI Institute in Mountain View, California.

Of those stars, the authors further identified 75 that are close enough — within 30 parsecs — for radio waves from Earth to already have washed over them since humans started to produce them. Those might be particularly good targets, Kaltenegger says, because aliens there could have both seen and heard us by now.

But other stars assume new prominence. For instance, astronomers know of seven Earth-sized planets orbiting the star TRAPPIST-1, 12 parsecs from Earth. TRAPPIST-1 will move into a position to see Earth as a transiting planet in the year 3663, say the study authors (see ‘Some of the stars with known exoplanets that have a view of Earth’).

Astronomers and science-fiction writers have noted that civilizations could signal their existence by constructing artificial ‘megastructures’ that pass in front of their stars, briefly dimming their light in a characteristic way.

Perhaps, some say, humanity should plan ahead for when eyes from the TRAPPIST-1 system might be cast in our direction. “Maybe we should think about installing a transiting megastructure for them to observe,” says René Heller, an astrophysicist at the Max Planck Institute for Solar System Research in Göttingen, Germany.

_____________________

Courtesy: Nature 

The post The 2,000 stars where aliens would catch a glimpse of Earth first appeared on Sindh Courier.