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NASA's A.I. Discovers a Second Solar System With 8 Planets, Just Like Ours
A machine learning algorithm has shown it can discover planets from weak signals overlooked in the Kepler spacecraft’s database.
Humans in the Western world for a long time thought that Earth was the center of the universe. At one point, it was heresy not to think so. After the heliocentric universe was adopted, we felt smaller and less self-important. But we’d also gained something, new knowledge and a new avenue in which to explore the heavens. That was a paradigm shift in our understanding and now, it’s happening again.
We know now today that our planet isn’t special in certain other respects. In terms of inhabiting a “goldilocks zone” which could harbor life, Earth is not the only planet that’s neither too hot nor too cold. Liquid water and an atmosphere are no longer considered luxuries, either. In short, given this and the mathematical possibilities, many scientists believe it’s only a matter of time before we find life somewhere else. After all, hundreds of billions of stars inhabit our galaxy alone, which assumes hundreds of planets the right size within the habitable zone. And that’s a very conservative assessment.
Kepler is a space telescope designed to scour a section of the Milky Way in order to find exoplanets. An exoplanet is one found beyond our solar system. Before Kepler, astronomers were wondering if planets themselves were common or rare. Launched in 2009, Kepler has discovered over 2,500 confirmed exoplanets, and 30 in the habitable zone—each less than twice the size of Earth. Today, there are currently 3,500 confirmed exoplanets total.
The Kepler spacecraft. Credit: NASA.
The problem with Kepler is that it collected reams of reams of data, so much that no one could go through it all. Scientists chose to select those candidates with strong signals. Weak signals could be a treasure trove for A.I., however. So Christopher Shallue, a senior software engineer at Google Brain, and Andrew Vanderburg, a NASA Sagan Postdoctoral Fellow at the University of Texas at Austin, decided to have a crack at it. They employed machine learning, a fascinating new A.I. field that’s making some incredible headway. Their A.I. utilizes artificial “neural networks” modeled after our brain, albeit a far simpler version.
In a recent press conference, Shallue and Vanderburg explained how they trained their A.I. program to identify exoplanets from the Kepler database. According to the scientists, you train neural networks not by programming them but by exposing them to what you want them to recognize. For instance, if you want it to identify puppies and kittens, you show it plenty of pictures of them. After a while, it’ll get good at recognizing them.
Except, here they didn’t show it pets. Instead, they taught it to read minuscule light changes in the brightness of a star which occur when a planet transits or passes in front of it. After enough practice, they let it loose on light recordings captured by Kepler. What the A.I. discovered is that our solar system isn’t as unique as we thought. Instead of being the only eight-planet one, we’re now one of two (that we know of).
NASA introduced the Kepler 90 system in an historic announcement on December 14, 2017. "The Kepler-90 star system is like a mini version of our solar system. You have small planets inside and big planets outside, but everything is scrunched in much closer," said Andrew Vanderburg, astronomer and NASA Sagan Postdoctoral Fellow at The University of Texas, Austin.
Experts speculate that an eight-planet solar system may in fact be common. It also seems that having a solar system where the smaller planets are in the front and the larger ones in the back, may not be so rare. Hopefully, future explorations can help to understand exactly how planetary systems form, as the discovery of new exoplanets has disrupted many of the theories astronomers developed from studying our solar system.
Credit: NASA/Ames Research Center/Wendy Stenzel.
The discovery began with just one planet. The A.I. found a weak transit signal from a planet known as Kepler-90i that had been previously missed. It’s in a planetary system called Kepler-90, located in the constellation Draco, some 2,545 light years from Earth. The new planet is extremely hot, with an average surface temperature of 800 degrees Fahrenheit—about as hot as mercury. Its year is incredibly short. It orbits its star once every 14.4 days. This system probably isn’t the best candidate for life. What’s game changing besides our solar system slipping from its unique place, is the ability to use machine learning to detect previously unrecognized exoplanets. The program also located a 6th planet in the newly discovered TRAPPIST system.
A.I. had been used previously to scour the Kepler database. But these findings show that artificial neural networks are particularly adept at the task. The idea was first posited by Google’s Shallue, who while studying astronomy in his free time, heard about how the discipline was drowning in data. "Machine learning really shines in situations where there is so much data that humans can't search it for themselves,” he said.
Paul Hertz is the director of NASA’s Astrophysics Division in Washington. He said, “Just as we expected, there are exciting discoveries lurking in our archived Kepler data, waiting for the right tool or technology to unearth them.” He added, “This finding shows that our data will be a treasure trove available to innovative researchers for years to come.” Shallue and Vanderburg consider this a successful proof of concept study. In it, explained in a paper to be published in The Astronomical Journal, the A.I. scanned 670 stars. In the future, they plan to have it study all 150,000 stars Kepler has identified.
Today, we still consider our planet special, as it’s the only known place to harbor life. One wonders for how long it’ll hold this lofty location. So far, the A.I. used can’t determine whether an exoplanet is a good candidate for life. But with technology and computing power moving so fast, that ability shouldn’t be too far away.
To see NASA’s video of this discovery, click here:
The finding is remarkably similar to the Dunning-Kruger effect, which describes how incompetent people tend to overestimate their own competency.
- Recent studies asked participants to rate the attractiveness of themselves and other participants, who were strangers.
- The studies kept yielding the same finding: unattractive people overestimate their attractiveness, while attractive people underrate their looks.
- Why this happens is unclear, but it doesn't seem to be due to a general inability to judge attractiveness.
There's no shortage of disparities between attractive and unattractive people. Studies show that the best-looking among us tend to have an easier time making money, receiving help, avoiding punishment, and being perceived as competent. (Sure, research also suggests beautiful people have shorter relationships, but they also have more sexual partners, and more options for romantic relationships. So call it a wash.)
Now, new research reveals another disparity: Unattractive people seem less able to accurately judge their own attractiveness, and they tend to overestimate their looks. In contrast, beautiful people tend to rate themselves more accurately. If anything, they underestimate their attractiveness.
The research, published in the Scandinavian Journal of Psychology, involved six studies that asked participants to rate the attractiveness of themselves and other participants, who were strangers. The studies also asked participants to predict how others might rate them.
In the first study, lead author Tobias Greitemeyer found that the participants who were most likely to overestimate their attractiveness were among the least attractive people in the study, based on average ratings.
Ratings of subjective attractiveness as a function of the participant's objective attractiveness (Study 1)
"Overall, unattractive participants judged themselves to be of about average attractiveness and they showed very little awareness that strangers do not share this view. In contrast, attractive participants had more insights into how attractive they actually are. [...] It thus appears that unattractive people maintain illusory self‐perceptions of their attractiveness, whereas attractive people's self‐views are more grounded in reality."
Why do unattractive people overestimate their attractiveness? Could it be because they want to maintain a positive self-image, so they delude themselves? After all, previous research has shown that people tend to discredit or "forget" negative social feedback, which seems to help protect a sense of self-worth.
To find out, Greitemeyer conducted a study that aimed to put participants in a positive, non-defensive mindset before rating attractiveness. He did that by asking participants questions that affirmed parts of their personality that had nothing to do with physical appearance, such as: "Have you ever been generous and selfless to another person?" Yet, this didn't change how participants rated themselves, suggesting that unattractive people aren't overestimating their looks out of defensiveness.
The studies kept yielding the same finding: unattractive people overestimate their attractiveness. Does that bias sound familiar? If so, you might be thinking of the Dunning-Kruger effect, which describes how incompetent people tend to overestimate their own competency. Why? Because they lack the metacognitive skills needed to discern their own shortcomings.
Greitemeyer found that unattractive people were worse at differentiating between attractive and unattractive people. But the finding that unattractive people may have different beauty ideals (or, more plainly, weaker ability to judge attractiveness) did "not have an impact on how they perceive themselves."
In short, it remains a mystery exactly why unattractive people overestimate their looks. Greitemeyer concluded that, while most people are decent at judging the attractiveness of others, "it appears that those who are unattractive do not know that they are unattractive."
Unattractive people aren't completely unaware
The results of one study suggested that unattractive people aren't completely in the dark about their looks. In the study, unattractive people were shown a set of photos of highly attractive and unattractive people, and they were asked to select photos of people with comparable attractiveness. Most unattractive people chose to compare themselves with similarly unattractive people.
"The finding that unattractive participants selected unattractive stimulus persons with whom they would compare their attractiveness to suggests that they may have an inkling that they are less attractive than they want it to be," Greitemeyer wrote.
Every star we can see, including our sun, was born in one of these violent clouds.
This article was originally published on our sister site, Freethink.
An international team of astronomers has conducted the biggest survey of stellar nurseries to date, charting more than 100,000 star-birthing regions across our corner of the universe.
Stellar nurseries: Outer space is filled with clouds of dust and gas called nebulae. In some of these nebulae, gravity will pull the dust and gas into clumps that eventually get so big, they collapse on themselves — and a star is born.
These star-birthing nebulae are known as stellar nurseries.
The challenge: Stars are a key part of the universe — they lead to the formation of planets and produce the elements needed to create life as we know it. A better understanding of stars, then, means a better understanding of the universe — but there's still a lot we don't know about star formation.
This is partly because it's hard to see what's going on in stellar nurseries — the clouds of dust obscure optical telescopes' view — and also because there are just so many of them that it's hard to know what the average nursery is like.
The survey: The astronomers conducted their survey of stellar nurseries using the massive ALMA telescope array in Chile. Because ALMA is a radio telescope, it captures the radio waves emanating from celestial objects, rather than the light.
"The new thing ... is that we can use ALMA to take pictures of many galaxies, and these pictures are as sharp and detailed as those taken by optical telescopes," Jiayi Sun, an Ohio State University (OSU) researcher, said in a press release.
"This just hasn't been possible before."
Over the course of the five-year survey, the group was able to chart more than 100,000 stellar nurseries across more than 90 nearby galaxies, expanding the amount of available data on the celestial objects tenfold, according to OSU researcher Adam Leroy.
New insights: The survey is already yielding new insights into stellar nurseries, including the fact that they appear to be more diverse than previously thought.
"For a long time, conventional wisdom among astronomers was that all stellar nurseries looked more or less the same," Sun said. "But with this survey we can see that this is really not the case."
"While there are some similarities, the nature and appearance of these nurseries change within and among galaxies," he continued, "just like cities or trees may vary in important ways as you go from place to place across the world."
Astronomers have also learned from the survey that stellar nurseries aren't particularly efficient at producing stars and tend to live for only 10 to 30 million years, which isn't very long on a universal scale.
Looking ahead: Data from the survey is now publicly available, so expect to see other researchers using it to make their own observations about stellar nurseries in the future.
"We have an incredible dataset here that will continue to be useful," Leroy said. "This is really a new view of galaxies and we expect to be learning from it for years to come."
Tiny specks of space debris can move faster than bullets and cause way more damage. Cleaning it up is imperative.
- NASA estimates that more than 500,000 pieces of space trash larger than a marble are currently in orbit. Estimates exceed 128 million pieces when factoring in smaller pieces from collisions. At 17,500 MPH, even a paint chip can cause serious damage.
- To prevent this untrackable space debris from taking out satellites and putting astronauts in danger, scientists have been working on ways to retrieve large objects before they collide and create more problems.
- The team at Clearspace, in collaboration with the European Space Agency, is on a mission to capture one such object using an autonomous spacecraft with claw-like arms. It's an expensive and very tricky mission, but one that could have a major impact on the future of space exploration.
This is the first episode of Just Might Work, an original series by Freethink, focused on surprising solutions to our biggest problems.
Catch more Just Might Work episodes on their channel: https://www.freethink.com/shows/just-might-work