字幕表 動画を再生する 英語字幕をプリント Syesha space is supported by Brilliant Dot or GE. This has been a really fun week for Planet news. First, thanks to artificial intelligence. Researchers from NASA and Google announced last week that they found two new sneaky exoplanets. The first comes from the star system, Kepler 90. It's around 2500 light years away, and we used to think it hosted seven planets. Surprised there's actually an eighth planet sneaking around in there, now called Coupler 90 I. The second is from the Star couple of 80 a star that lives only about 1100 light years away. We had thought there were only five planets around there, but now scientists have found 1/6 called Kepler a B G. The reason we, Mrs Before, is tied to how the Coupler Space telescope, which we used to study these stars, looks for planets that uses the transit method, which measures the star's light intensity to see if there are any dips in brightness. If you find a regularly timed light dip, it means something is passing in front of the star and you've probably got something orbiting in. There is not. That is really good at detecting planets that are big or have a really wide orbit, or both. Thanks to perspective, bigger planets cause bigger tips, and if the planets further out from its star, it'll obscure Maur of it compared to a similarly sized planet close in. But these new planets were stealthy and harder to detect. Help their 90 iess small only slightly bigger than Earth and closer to its star than mercury is to the sun. Meanwhile, coupler 80 G is probably lined up with and obscured by other planets in its neighborhood. The telescope could still detect them, but their signals were too small for our older programs to flag or for a human to notice. But now, thanks to advances in artificial intelligence, computers can teach themselves, had a better recognize signals, and they're getting so good that they confined planets those other programs overlooked. The scientists who made the discoveries used a machine learning programs to pick apart the stars. Datasets machine learning is when a computer uses algorithms called neural networks to detect patterns in sets of data and to make extrapolations based on those patterns. This is how we learn to, and like us, a computer can get better at this overtime. Before analyzing the couple of 80 and 90 data, the scientists trained their neural networks using data sets that we already knew contained transiting planets. They wanted to see how well the computer could tell the difference between an actual planet and a false positive based on things like how its brightness changed over time. And it turns out it could do this really well. Like almost 99% of the time. It's correct. So then they fed the network more data from the Kepler Space Telescope, this time from almost 700 systems where we'd already found planets just in case there are more undetected ones. And by doing this, they found coupler 90 i and 80 g detecting those tiny signals that we've missed. Of course, this is exciting because their new planets and every new planet means that our vision of the universe has to get more vast. But it also means for getting better at processing the absolute mountain of Kepler Space Telescope data that exists couple has been sending data back to us way faster than we've been able to get through it. So using neural networks to process all that data makes a lot of sense, and as they get faster and more powerful, will have even more amazing tools for getting to know the universe. And closer to home, we're still learning things about our best friend Mars. This Wednesday in the journal Nature, scientists published a paper that might help explain where some of Mars is water went. We've known for years that our neighbouring planet doesn't have a lot of water, even though it used to be. Part of the reason Mars is so dry is because it no longer has a strong magnetic field. Without that, Mars couldn't retain its atmosphere, so the atmospheric pressure dropped low enough that water evaporated. But that also isn't the whole story. The magnetic field problem can account for a lot of the water loss, but not all of it. The scientists have been on the hunt for the rest of that water or whatever made it disappear, and now they think they found the culprit. Spongy rocks billions of years ago, Mars had a ton of water and active volcanoes, so the planet's crust is mostly bas L, a volcanic rock that conform when water and lava meet. It also contains Sir Pen Tonight's a type of rock that can also form after interactions with water, which all seemed pretty suspicious. So the scientists built a computer model of the planet's early geology and geochemistry to investigate, and they found that the assault specifically might be responsible for the missing water. Their model shows that when water and lobbies reacted to form the assault, a lot of that water got incorporated right into the rock. Mars is basil has a lot more iron oxides in it than the kind on earth, which creates different types of minerals in the rock. And that mix of minerals can hold about 25% more water. So Mars is basalt, probably trapped aton of water like a sponge. That water eventually made its way down into the mantle, the region of a planet just below the crest, probably because the hydrated assault got buried under a bunch of lava flows. So that's where they think the water is deep underground, chilling in Mars's mantle. Well, not exactly chilling. It's pretty warm down there. Now. It's still possible that there other reasons for Mars is disappearing. Water like it could have ended up in ice closer to the surface will need more observations to be sure. But we are definitely getting closer to an answer. No machine learning required if you like exoplanets. But machine learning isn't for you. Syesha sponsor brilliant has lessons in there. Astronomy unit. They cover some of the other waves we detect and measure exoplanets. There's a lesson about gravitational wobble which guides you through problems to show how we know so much about our nearest exoplanet Proxima Centauri B. I've always found this area of astronomy fascinating, so I thought I'd give it a try. So this quiz on gravitational wobble is coming at the end in this world's beyond earth section on Brilliant. So it talks about the Goldilocks Zone, which is really cool, and then exoplanets and transits and how we find them. And then it jumps in to learning about gravitational wobble. So I have to figure out how fast Jupiter orbits the sun in kilometers per second. So in this section, it's actually pretty simple geometry. It's just with huge numbers. So if I take the distance that Jupiter is from the sun and use that as my radius for its total orbit That's just, you know, to pi. Times are actually work this out with paper and pencil beforehand because it's really big numbers. And I needed a calculator and tow like just map it out and think it through, since it's been a while since I've done geometry. So I know that the answer is 13.3 and that's right, 13.3 kilometers per second is how fast Jupiter orbits the sun. But then it goes on to talk about how the sun wobbles because Jupiter is affecting the sun with its gravity. And his readers pointed out, before, you can totally view the solution to help you figure stuff out. So I'm gonna work my way through this gravitational wobble lesson. So then I can get to the interstellar travel lesson so brilliant artwork is really fun because you're learning and kind of like stretching those muscles that maybe haven't stretched in a while. Or maybe you just want more practice of doing that right now. The 1st 200 people to sign up but brilliant dot org's slash psycho space will get 20% off of their annual subscription, and you'll be helping to support social space also. So thank you so much.