Speaker 1: Welcome to Bedtime Astronomy. Explore the wonders of the cosmos
Speaker 1: with our soothing Bedtime Astronomie podcast. Each episode offers a
Speaker 1: gentle journey through the stars, planets, and beyond, perfect for
Speaker 1: unwinding after a long day. Let's travel through the mysteries
Speaker 1: of the universe as you drift off into a peaceful
Speaker 1: slumber under the night sky.
Speaker 2: Imagine staring at the night sky without blinking, like, not
Speaker 2: for a minute, not for an hour, but for four straight.
Speaker 3: Years, just an unyielding, relentless gaze locked onto.
Speaker 2: The dark exactly, no glancing away, no resting your eyes.
Speaker 2: That is exactly what NASA's Transiting Exoplanet Survey satellite that
Speaker 2: are known as TESS has been doing. It has been
Speaker 2: staring completely unblinking at over two point two million stars.
Speaker 2: And it's not just looking at them in a passive,
Speaker 2: romantic sort of way.
Speaker 3: No, it's watching for the absolutely faintest, most microscopic dimming
Speaker 3: of their starlight.
Speaker 2: Yeah, we are talking about drops and lights so small
Speaker 2: that they push the absolute boundaries of our technological limits.
Speaker 3: To really appreciate the scale of that microscopic dimming, we
Speaker 3: basically have to throw out our everyday understanding of brightness.
Speaker 3: How so, well, we are talking about a drop in
Speaker 3: stellar luminosity that is so minuscule it would be entirely
Speaker 3: imperceptible to the human eye, even to you are floating
Speaker 3: in a spacesuit right next to the telescope lenses. Yeah.
Speaker 3: Tests is essentially this collection of ultra sensitive cameras designed
Speaker 3: to capture incredibly subtle dips in light, dips that occur
Speaker 3: when a planet passes directly in front of its host
Speaker 3: star from our specific vantage point here in the Solar System.
Speaker 2: So it's kind of like a game of shadows played
Speaker 2: across trillions of miles of empty space.
Speaker 3: That's exactly what it is.
Speaker 2: And because TESS has been maintaining this unblinking vigil for
Speaker 2: its first four years of operation, it has accumulated a
Speaker 2: mountain of data that is frankly almost impossible for a
Speaker 2: human brain to comprehend.
Speaker 3: Oh, the volume is staggering. Yeah, we are talking about continuous,
Speaker 3: unbroken light curves for two point two million distinct fusion
Speaker 3: reactors scattered across the galaxy.
Speaker 2: But today we are jumping straight into a monumental breakthrough
Speaker 2: that just emerged from that exact mountain of.
Speaker 3: Data, right the new research.
Speaker 2: Yeah, astronomers at the University of Warwick have successfully waded
Speaker 2: through that digital ocean and validated over one hundred exoplanets. Specifically,
Speaker 2: they've handed us one hundred and eighteen validated planets, and
Speaker 2: the really exciting part is the thirty one newly detected ones.
Speaker 3: Right, Exactly thirty one of those are completely fresh discoveries
Speaker 3: that literally nobody on Earth knew existed until the publication
Speaker 3: of this research, which is wild, it really is. Thirty
Speaker 3: one brand new worlds is a staggering yield for a
Speaker 3: single study, especially when you consider how thoroughly the low
Speaker 3: hanging fruit in exoplanet research has already been picked.
Speaker 2: Yeah, we've been at this for a while now, we have.
Speaker 3: But the Warwick team didn't stop there. Alongside those one
Speaker 3: hundred and eighteen validated planets, they also produce this massive,
Speaker 3: highly refined catalog of over two thousand high quality planet.
Speaker 2: Candidates, and nearly one thousand of those candidates are entirely
Speaker 2: new to science too.
Speaker 3: They've basically handed the global astronomical community a highly detailed,
Speaker 3: curated treasure map.
Speaker 2: Okay, let's unpack this, because to really wrap your head
Speaker 2: around how difficult it is to find these things. Let's
Speaker 2: think about the method they're actually using, the transit method.
Speaker 2: Right in astrophysics, this is called the transit method. Imagine
Speaker 2: trying to spot a moth flying in front of a
Speaker 2: distant street light.
Speaker 3: I love this analogy, thanks, But the street.
Speaker 2: Light isn't down the block at the end of your street.
Speaker 2: It is hundreds of light years away. You can't see
Speaker 2: the moth itself. The moth is entirely invisible.
Speaker 3: You're just looking for the shadow exactly.
Speaker 2: You are solely trying to measure the exact fraction of
Speaker 2: a shadow that the moth casts on your eye as
Speaker 2: its tiny, fragile body blocks a microscopic sliver of the
Speaker 2: bulb's light for just a few hours.
Speaker 3: It's a great way to picture it, but I'd actually
Speaker 3: take it a step further to highlight the sheer statistical
Speaker 3: improbability of what test is doing.
Speaker 2: Okay, go for it.
Speaker 3: So the distant street light is the host star and
Speaker 3: the moth is the exoplanet. Yes, but remember planets don't
Speaker 3: just fly around randomly. They orbit in a flat plane,
Speaker 3: right like a record player exactly, So for us to
Speaker 3: see that shadow, that temporary drop in the star's light curve.
Speaker 3: The geometry has to be absolutely perfect. The edge of
Speaker 3: that planetary system has to be perfectly aligned with our
Speaker 3: line of sight.
Speaker 2: Oh, I say yeah.
Speaker 3: If the system is tilted even a few degrees up
Speaker 3: or down relative to Earth, the planet just orbits above
Speaker 3: or below the star from our perspective, and we see
Speaker 3: absolutely nothing.
Speaker 2: So not only are we looking for a moth's shadow
Speaker 2: from light years away, we are only able to see
Speaker 2: the tiny fraction of moths that happen to be flying
Speaker 2: on the exact right trajectory across our line of vision.
Speaker 3: Precisely, which means for every planet test actually catches, there
Speaker 3: are likely dozens, if not hundreds of others orbiting just
Speaker 3: out of geometric alignment.
Speaker 2: That's humbling. The universe is teeming with these things, but
Speaker 2: we are only catching the ones that cross our incredibly narrow.
Speaker 3: Tripwire, and we are looking for a highly specific periodic
Speaker 3: dip in brightness. The moth has to circle the street
Speaker 3: light and block the light again and again on a
Speaker 3: predictable schedule.
Speaker 2: Right. That periodicity is the first major clue it is.
Speaker 3: But you know, the universe is a remarkably messy, chaotic place.
Speaker 3: A lot of things can cause a star's light to
Speaker 3: flicker or dim like what, well, stars have massive internal
Speaker 3: weather systems, They have sunspots larger than the Earth that
Speaker 3: rotate in and out of you. They have violent flares.
Speaker 2: Oh right, so it's not a steady bolt exactly.
Speaker 3: And the instruments on the satellite itself experienced thermal shifts
Speaker 3: that introduce noise into the data. Distinguishing the delicate, repeating
Speaker 3: shadow of our proverbial moth from all the other violent
Speaker 3: cosmic noise is the real challenge of modern astronomy.
Speaker 2: Which brings us to why this matters to you listening
Speaker 2: right now. The focus of this breakthrough isn't just about
Speaker 2: finding a few new dots in the sky or adding
Speaker 2: a list of unpronounceable alphanumeric names to a stellar catalog.
Speaker 3: Definitely not.
Speaker 2: It's about a revolutionary new artificial intelligence tool that is
Speaker 2: completely changing how we map the universe. We are witnessing
Speaker 2: a fundamental shift in our astronomical capabilities.
Speaker 3: We really are. The era of humans painstakingly plotting graphs
Speaker 3: to find planets is over. It had to end, didn't
Speaker 3: the Oh absolutely, when you were looking at continuous light
Speaker 3: readings from two point two million stars, taking measurements every
Speaker 3: two minutes. In some cases, the data volume becomes so
Speaker 3: vast that it is physically impossible for human eyes to
Speaker 3: process at all.
Speaker 2: Not even massive international teams of human eyes.
Speaker 3: Not a chance. You can't just throw a room full
Speaker 3: of tired graduate students at this problem and ask them
Speaker 3: to stare at squiggly lines on monitors all day.
Speaker 2: Right, that sounds like torture.
Speaker 3: It is. You need a way to separate the true
Speaker 3: planets from the cosmic illusions, and you need to do
Speaker 3: it at an industrial scale. And that absolute necessity is
Speaker 3: exactly what birth this new AI pipeline.
Speaker 2: You know. I always find it fascinating how the biggest
Speaker 2: roadblock in a scientific field often isn't the technology used
Speaker 2: to gather the data, but our capacity to actually read it.
Speaker 3: That's very common issue in modern science.
Speaker 2: So what exactly is the bottleneck here? If tests is
Speaker 2: so incredibly sensitive, why is it so hard to just
Speaker 2: look at a dip in the light curve and confidently
Speaker 2: announced to the world, yep, we found another Earth. Pack
Speaker 2: your bags.
Speaker 3: Well, the massive bottleneck in modern astronomy right now isn't
Speaker 3: detection at all. It's conformation. Okay, missions like tests or
Speaker 3: its legendary predecessor, the Kepler Space Telescope, are incredibly proficient
Speaker 3: at flagging anomalies. They routinely identify thousands of possible planet candidates.
Speaker 2: They say, hey, the light dipped tier, you should look
Speaker 2: at this exactly.
Speaker 3: But confirming which of those signals are real physical planets
Speaker 3: and which are illusions is painstakingly slow. The primary culprit
Speaker 3: holding everything up in the astronomical community is something we
Speaker 3: call false positives.
Speaker 2: Because the universe loves to play tricks on us.
Speaker 3: Oh, it really does. It generates phenomena that look exactly
Speaker 3: like what we are searching for but are entirely different beasts.
Speaker 2: Right, and the most common, the most frustrating trick the
Speaker 2: universe plays on exoplanet hunters is the eclipsing binary star system.
Speaker 3: Right, that is the big one. Yes. To understand this,
Speaker 3: we have to remember that our own sun, which is
Speaker 3: a solitary star floating alone in space, is actually somewhat
Speaker 3: of an anomaly. Really yeah, A huge percentage of the
Speaker 3: stars in our galaxy are binary systems, meaning two stars
Speaker 3: locked in a gravitational dance orbiting around a common center of.
Speaker 2: Mass like a tattooine situation to use the mandatory Sci
Speaker 2: Fi reference two suns in the sky exactly that.
Speaker 3: Now, imagine those two stars orbiting around each other from
Speaker 3: our specific line of sight. If the orbital plane is
Speaker 3: edge on to Earth, one star will periodically pass in
Speaker 3: front of the other. Okay, I'm picturing it. Sometimes the
Speaker 3: stars are of different sizes and temperatures. When a smaller, dimmer,
Speaker 3: cooler star passes in front of a larger, brighter, hotter star,
Speaker 3: it blocks a portion of the bright star's light.
Speaker 2: Which creates a dimming effect in the overall light we
Speaker 2: receive in our telescopes.
Speaker 3: Right and mechanically, mathematically, that specific dip in light can
Speaker 3: perfectly mimic the transit of a large Jupiter sized planet.
Speaker 2: Let me make sure I'm visualizing this correctly. The telescope
Speaker 2: is basically just a bucket collecting photons.
Speaker 3: That's a good way to put it.
Speaker 2: It doesn't see a picture of a star. It just
Speaker 2: measures the amount of light hitting its sensor. So the
Speaker 2: telescope sees the light dropped by say one percent, flags
Speaker 2: it as a potential giant planet crossing the star, but
Speaker 2: it's actually just a smaller, fainter star getting in the way.
Speaker 3: Yes, it's a stellar eclipse, not a planetary transit.
Speaker 2: Man. That is deeply frustrating.
Speaker 3: That's the core of the false positive problem. And it
Speaker 3: gets even more insidious. You have what are called grazing binaries.
Speaker 3: Grazing binaries, Yeah, this is where the two stars aren't
Speaker 3: perfectly aligned with us, but they just barely clip the
Speaker 3: edge of each other's disc as they.
Speaker 2: Orbit, just a glancing glow.
Speaker 3: Exactly, that tiny little clip that grazing pass creates a
Speaker 3: very shallow dip in the light curve, and a shallow
Speaker 3: dip is the exact signature we expect from a small
Speaker 3: Earth sized rocky planet. So a grazing binary star system
Speaker 3: can totally masquerade as a potentially habitable Earth like world.
Speaker 2: So how did astronomers handle this before the AI? I mean,
Speaker 2: you have a list of thousands of these signals from tests.
Speaker 2: How do you figure out if it's a moth or
Speaker 2: just another slightly dimmer street light swinging in the wind?
Speaker 3: Well? Distinguishing between a planetary transit and eclipsing binary historically
Speaker 3: required intense, incredibly time consuming follow up observation.
Speaker 2: And pointing other telescopes at it.
Speaker 3: Exactly you couldn't just use tests anymore. You had to
Speaker 3: requisition time on completely different, massive ground based telescopes on Earth.
Speaker 3: Oh wow, You'd use instruments called spectrographs to analyze the
Speaker 3: light spectrum of the star looking for minute gravitational wobbles.
Speaker 3: It's a technique called radial velocity.
Speaker 2: How does that tell them? Apart?
Speaker 3: A planet will cause au star to wobble very slightly,
Speaker 3: but a massive companion star will cause the primary star
Speaker 3: to yank back and forth violently.
Speaker 2: But getting time on those giant ground based observatories is
Speaker 2: notoriously competitive. You can't just point the Keck telescope in
Speaker 2: Hawaii at two thousand different stars just to double check
Speaker 2: Tess's homework.
Speaker 3: No, it would take decades and cost millions of dollars.
Speaker 2: Which is precisely why we have a massive backlog of
Speaker 2: unconfirmed candidates just sitting on servers waiting to be vetted.
Speaker 3: Exactly. The traditional scientific process simply could not keep pace
Speaker 3: with the sheer volume of data the satellite was beaming
Speaker 3: down to Earth every month. We were drowning in potential discoveries,
Speaker 3: completely paralyzed by the false.
Speaker 2: Positive enter raven. Yeah, this is the newly developed AI
Speaker 2: pipeline from the team at the University of Warwick that
Speaker 2: is cutting through the backlog like a Scythe and raven
Speaker 2: stands for a ranking and validation of exoplanets. I always
Speaker 2: appreciate a backronym in astronomy.
Speaker 3: They are very good at them.
Speaker 2: They really are its core function. Its entire reason for
Speaker 2: existing is to solve this exact bottleneck. It is a
Speaker 2: bespoke software architecture designed to distinguish between actual planetary transits
Speaker 2: and these astrophysical impostures without needing years of expensive ground
Speaker 2: based telescope.
Speaker 3: Time and the way to choose this is an absolute
Speaker 3: master class in modern computational astrophysics.
Speaker 2: How does it actually work.
Speaker 3: It doesn't just look at the overall depth of the
Speaker 3: shadow and make a guess. It uses deep machine learning,
Speaker 3: specifically advanced neural network architectures to analyze the entire shape.
Speaker 2: Of the light curve, the shape of the curve itself.
Speaker 3: Right. It looks at the duration of the dip, the
Speaker 3: speed at which the light drops the tiny, almost imperceptible
Speaker 3: variations at the edges of the shadow as the planet
Speaker 3: begins its crossing. It searches for the subtle mathematical signatures
Speaker 3: that separate a solid spherical planet from a glowing dynamic star.
Speaker 2: Okay, let's unpack this methodology, because this is where I
Speaker 2: have to push back a little on how AI usually
Speaker 2: operate rates. When I was diving into the research papers
Speaker 2: detailing how they built Raven, I noticed something that sounded
Speaker 2: completely counterintuitive, almost paradoxical. The training data exactly. Usually, when
Speaker 2: you train an AI, you feed it real world examples.
Speaker 2: If you want an AI to recognize a cat, you
Speaker 2: show it a million photographs of actual cats. Right, But
Speaker 2: the Warwick researchers didn't train this AI on the thousands
Speaker 2: of real exoplanets we've already discovered and confirmed over the
Speaker 2: last twenty years. Instead, they trained Raven on hundreds of
Speaker 2: thousands of realistically simulated planets and simulated stellar events.
Speaker 3: Yes they did.
Speaker 2: I have to admit I was scratching my head. We
Speaker 2: are training an AI on fake computer generated planets so
Speaker 2: it can find real ones. How does learning from a
Speaker 2: massive video game simulation of the universe give us an
Speaker 2: edge over just studying the real, messy data we already
Speaker 2: have from Kepler and Tess.
Speaker 3: What's fascinating here is that your hesitation hits on the
Speaker 3: absolute most crucial debate in machine learning. Right now. Your
Speaker 3: confusion is exactly why this approach is so brilliant and
Speaker 3: why it represents such a leap forward.
Speaker 2: Okay, explain that to me, it all comes down.
Speaker 3: To how neural networks actually learn. Machine learning models excel
Speaker 3: at pattern recognition. They are vastly superior to humans at
Speaker 3: finding hidden correlations in high dimensional data. But to learn
Speaker 3: those patterns correctly, they need training data that is perfectly categorized.
Speaker 3: They need to know, without a shadow of a doubt,
Speaker 3: what the right.
Speaker 2: Answer is, So they need an infallible answer key like
Speaker 2: if you are teaching a child math, you can't give
Speaker 2: them a textbook where half the answers in the back
Speaker 2: are wrong or you know, probably right, but we aren't
Speaker 2: totally sure exactly.
Speaker 3: In computer science we call this the ground truth. If
Speaker 3: we were to train an AI exclusively on real data
Speaker 3: from tests or even our catalog of previously confirmed planets,
Speaker 3: we are fundamentally training it on data that is full
Speaker 3: of noise, uncorrected instrument artifacts, and most importantly, unknown.
Speaker 2: Variables, even for the ones we think or confirmed, even.
Speaker 3: Those even with planets we think we've confidently confirmed using
Speaker 3: ground based telescopes, there's always a margin of error. There's
Speaker 3: always a tiny, nagging percentage of doubt.
Speaker 2: Kind of doubt.
Speaker 3: Well, what if a faint background star is blending its
Speaker 3: light with our target, subtly altering the signal? What if
Speaker 3: our understanding of the star's mass is slightly off?
Speaker 2: Ah, I see where this is going. If you train
Speaker 2: an AI on flawed, messy human data, it just inherits
Speaker 2: all of our own blind spots. Precisely, If the astronomical
Speaker 2: community has been systematically misclassifying a certain type of weird
Speaker 2: grazing binary star as a rocky planet for the last
Speaker 2: ten years, and we feed that into the AI, the
Speaker 2: AI will just learn to make that exact same mistake,
Speaker 2: only it will make that mistake a million times faster
Speaker 2: and with terrifying confidence.
Speaker 3: And that is the absolute danger of black box AI
Speaker 3: in science. It amplifies human error. But by generating simulated events,
Speaker 3: the researchers hold the ultimate perfect answer.
Speaker 2: Key Because as they built the simulation.
Speaker 3: Right, they built incredibly complex mathematical models of stars, They
Speaker 3: generated artificial planetary transits across them, calculating the exact geometry
Speaker 3: of the shadows. They added synthetic telescope noise that perfectly
Speaker 3: mimics the thermal shifts and pixel bleed of the test cameras.
Speaker 2: That's incredibly thorough.
Speaker 3: And critically they generated artificial eclipsing binary stars and grazing binaries.
Speaker 3: Because the researchers created the simulation from the ground up,
Speaker 3: writing the laws of physics in code, they know the
Speaker 3: absolute ground truth of every single pixel of data.
Speaker 2: They know with one hundred percent mathematical certainty, whether a
Speaker 2: specific simulated light curve was caused by a fake planet
Speaker 2: or a fake binary star exactly. And it makes total
Speaker 2: sense when you frame it like that, you aren't just
Speaker 2: showing it pictures of shadows. You are teaching the AI
Speaker 2: the exact, pure mathematical signature of a planet, completely divorced
Speaker 2: from human error and observational bias. Yes, you're giving it
Speaker 2: the fundamental physics of an orbital transit isolated in a
Speaker 2: stere laboratory environment.
Speaker 3: And by simulating hundreds of thousands of these events, they
Speaker 3: can expose the AI to scenarios that might be incredibly
Speaker 3: rare in our current real world catalogs, but theoretically possible
Speaker 3: in the wider universe, Like what well they can train
Speaker 3: the AI on what a planet looks like orbiting a
Speaker 3: highly active star covered in massive sunspots, or what a
Speaker 3: planet theory transit looks like if the planet has a
Speaker 3: massive ring system like Saturn.
Speaker 2: Oh, that's so smart, right.
Speaker 3: Once the neural network has completely mastered the physics of
Speaker 3: the simulation, once it can perfectly distinguish the fake planets
Speaker 3: from the fake stars, then and only then is it
Speaker 3: unleashed on the real, messy tests data.
Speaker 2: And because it has internalized the underlying signature of a
Speaker 2: true planet so deeply, it can cut through the noise
Speaker 2: with incredible precision. It knows what a moth's shadow looks like,
Speaker 2: even if the street light is flickering, and even it
Speaker 2: fits raining precisely.
Speaker 3: And it doesn't just give a yes or no answer.
Speaker 3: Raven provides a statistical probability.
Speaker 2: Oh, it gives a confidence score exactly.
Speaker 3: It looks at a candidate and says, based on my training,
Speaker 3: there is a ninety nine point nine percent chance this
Speaker 3: is a planetary transit, and only one point one percent
Speaker 3: chance it is a background eclipsing binary. When that probability
Speaker 3: crosses a certain rigorous threshold, the candidate is upgraded to
Speaker 3: a validated planet.
Speaker 2: And the other thing that makes Raven such a paradigm
Speaker 2: shift is the workflow advantage. I was reading about how
Speaker 2: this used to be done and in the past, confirming
Speaker 2: a planet was this incredibly fragmented, piecemeal process oosen Nimare.
Speaker 2: You'd have one astronomer write a piece of software to
Speaker 2: clean the raw data. Then you'd pass that data to
Speaker 2: another team using a different algorithm to search for periodic dips.
Speaker 2: Then a human might literally eyeball the resulting.
Speaker 3: Graphs yep, lots of staring at graphs.
Speaker 2: Then if it looks promising, you'd feed it into a
Speaker 2: completely different statistical tool to calculate the probabilities of a
Speaker 2: false positive.
Speaker 3: It was a very patchwork, fragmented ecosystem of tools. Every
Speaker 3: research group had their own preferred software packages written in
Speaker 3: different programming languages.
Speaker 2: That was incredibly inefficient.
Speaker 3: It inherently slows down the science, makes it difficult to
Speaker 3: reproduce results, and introduces opportunities for errors or data corruption
Speaker 3: at every single handoff point. It was well artisanal planet hunting, but.
Speaker 2: Raven handles the entire process end to end. It's a
Speaker 2: unified ecosystem.
Speaker 3: From start to finish.
Speaker 2: It takes the raw, messy, noisy data straight from the satellite,
Speaker 2: processes the light curves, detects the potential periodic signals, vets
Speaker 2: those signals using its highly trained machine learning models to
Speaker 2: weed out the astrophysical imposters, and then statistically validates the
Speaker 2: surviving planets, all in one seamless automated pipeline.
Speaker 3: That's the beauty of it.
Speaker 2: It takes the grueling, repetitive heavy lifting out of the
Speaker 2: equation and just hands the astronomers a highly reliable, mathematically
Speaker 2: sound list of worlds.
Speaker 3: It's the difference between a master mechanic trying to build
Speaker 3: a car from individual parts they ordered from a dozen
Speaker 3: different catalogs versus having a fully automated, state of the
Speaker 3: art robotic assembly lie perfect analogy. The efficiency and consistency
Speaker 3: are what allows us to look at two point two
Speaker 3: million stars and actually makes sense of him in the
Speaker 3: human lifetime.
Speaker 2: So now that we understand how this AI works and
Speaker 2: why training it on a massive simulation was the secret
Speaker 2: to its success, we need to look at what it
Speaker 2: actually found when they turned it on. The fun part,
Speaker 2: because the AI didn't just find a bunch of generic,
Speaker 2: boring dead rocks floating in empty space. It uncovered some
Speaker 2: of the most extreme, chaotic and alien worlds imaginable. Hidden
Speaker 2: in that test data, the thirty one newly discovered planets
Speaker 2: are not places you would ever want to visit. Let's
Speaker 2: talk about the exoplanet menagerie.
Speaker 3: The diversity of planetary systems out there is truly staggering.
Speaker 3: Our own Solar system, with its neat division of inner
Speaker 3: rocky planets and outer gas giants, is just one tiny,
Speaker 3: specific flavor in a cosmic ice cream shop of bizarre configurations.
Speaker 2: And it's important to note the specific focus of the
Speaker 2: Warwick team's study to understand what Raven was finding. The
Speaker 2: pipeline was specifically off demise to look for planets with
Speaker 2: very tight orbits. We are talking about planets that complete
Speaker 2: a full orbit around their star a full planetary year
Speaker 2: in less than sixteen days.
Speaker 3: Sixteen days. Earth takes three hundred and sixty five days
Speaker 3: to make that trip. Mercury, the closest planet to our Sun,
Speaker 3: takes eighty.
Speaker 2: Eight days, So a sixteen day year means these planets
Speaker 2: are hugging their host stars incredibly uncomfortably closely. They are
Speaker 2: practically skimming the surface.
Speaker 3: They are deeply embedded in the intense gravitational and radiational
Speaker 3: environment of their stars. To give you a sense of scale,
Speaker 3: if Earth were on a sixteen day orbit, the Sun
Speaker 3: would take up a massive portion of the sky, the
Speaker 3: heat would be unimaginable.
Speaker 2: And within that already extreme sixteen day parameter Raven managed
Speaker 2: to detect a population of what astronomers classify as ultra
Speaker 2: short period planets, or usps.
Speaker 3: These are worlds that take the concept of a tight
Speaker 3: orbit to an absurd extreme.
Speaker 2: These are planets that orbit their stars in less than
Speaker 2: twenty four hours.
Speaker 3: Less than an Earth day to complete a full year.
Speaker 2: I want to try and visualize that reality for a second.
Speaker 2: Jugene celebrating New Year's Eve. Every single day you wake up,
Speaker 2: it's a new year. You go to sleep, the year
Speaker 2: is over. Your entire planetary cycle season orbital dynamics is
Speaker 2: compressed into a single Earth day.
Speaker 3: It's frantic.
Speaker 2: A planet moving that incredibly fast, orbiting that perilously close
Speaker 2: to its host star is experiencing an environment that completely
Speaker 2: breaks the human imagination. At that proximity, the gravitational forces
Speaker 2: are immense. The gravity of the star almost certainly locks
Speaker 2: the planet in place. A phenomenon we call tidal locking.
Speaker 2: It's the same physics that keeps one side of our
Speaker 2: Moon always facing Earth right, So for these ultra short
Speaker 2: period planets, one side of the planet always faces the
Speaker 2: nuclear furnace of the Sun and the other side is
Speaker 2: cast in eternal freezing darkness.
Speaker 3: And the dayside of a tidally locked ultra short period
Speaker 3: planet isn't just hot in the way we can comprehend.
Speaker 3: It is a literal physical hellscape, like how hot. Because
Speaker 3: they are so close, the surface temperatures on the star
Speaker 3: facing side can easily reach several thousands of degrees. At
Speaker 3: those temperatures, rock doesn't just melt into magma. It vaporizes
Speaker 3: vaporized rock.
Speaker 2: Wow.
Speaker 3: Yeah, the extreme heat breaks down molecular bonds. You are
Speaker 3: likely looking at unimaginable landscapes dominated by global churning oceans
Speaker 3: of molten lava, and the atmosphere, if the stellar winds
Speaker 3: haven't entirely stripped away, would be composed of vaporized rock
Speaker 3: and heavy metals.
Speaker 2: So we are talking about planets where the weather forecast
Speaker 2: might literally call for scattered showers of liquid iron or
Speaker 2: condensation of vaporized glass.
Speaker 3: Pretty much.
Speaker 2: Yeah, and thanks to this AI pipeline. These aren't just
Speaker 2: theoretical mathematical models on a whiteboard anymore. Raven is casually
Speaker 2: validating dozens of these nightmare worlds in a single sweep
Speaker 2: of the test data.
Speaker 3: It's finding them so efficiently that we can start studying
Speaker 3: them as a broader population rather than just isolated freaks
Speaker 3: of nature.
Speaker 2: And studying them tells us so much about the violent
Speaker 2: history of solar systems. Plants don't generally form that close
Speaker 2: to a star, do they know?
Speaker 3: The environment is way too hot and volatile for dust
Speaker 3: and gas to clump together and form a planet.
Speaker 2: So how did they get there?
Speaker 3: Well, which means these ultra short period worlds likely formed
Speaker 3: much further out in their solar systems where it was cooler,
Speaker 3: and then, over millions of years, gravitational interactions with other
Speaker 3: planets dragged them inward. They spiraled closer and closer to
Speaker 3: the star until they parked in these extreme roasting orbits.
Speaker 2: It's the kind of extreme physics that used to belong
Speaker 2: entirely to the realm of science fiction, and now it's
Speaker 2: just a Tuesday for an AI algorithm at Warwick. But
Speaker 2: the extreme environments Raven found aren't just limited to these
Speaker 2: solitary roosting rocks. The pipeline also proved incredibly adept at
Speaker 2: detecting close orbiting multiplanet systems. We are talking about previously
Speaker 2: unknown planetary pairs, or even entire groups of planets orbiting
Speaker 2: the same star, all packed into incredibly tight spaces.
Speaker 3: This is where orbital mechanics gets incredibly complex, and where
Speaker 3: the AI truly flexes its computational muscle.
Speaker 2: How tired are we talking?
Speaker 3: Well? When we think of a Solar system picture, our
Speaker 3: own planets spread out over vast distances, neatly separated by
Speaker 3: millions of miles of empty space, Mercury than Venus than Earth,
Speaker 3: all keeping a respectful distance, right, But the universe has
Speaker 3: a way of packing planets together in configurations that completely
Speaker 3: challenge our traditional models of planetary formation.
Speaker 2: A perfect specific example of this, to help visualize exactly
Speaker 2: the kind of chaos Raven is trained to untangle, is
Speaker 2: the Kepler eleven system. Now, Kepler eleven was discovered before Raven,
Speaker 2: but it perfectly illustrates the phenomenons.
Speaker 3: A great example.
Speaker 2: Kepler eleven is a sun like star relatively similar to
Speaker 2: our own, but it has six confirmed planets orbiting it,
Speaker 2: and those planets aren't spread out elegantly like ours. Five
Speaker 2: of the six planets and Kepler eleven orbit closer to
Speaker 2: their star than Mercury orbits our Sun. It's wild to
Speaker 2: think about imagine taking Venus, Earth, Mars, Jupiter, and Saturn
Speaker 2: and crushing all of their orbits down so they fit
Speaker 2: inside the orbit of Mercury. It is an unbelo believably crowded,
Speaker 2: claustrophobic neighborhood.
Speaker 3: And from our observational perspective, watching the light curve of
Speaker 3: a crowded system like that is both chaotic and.
Speaker 2: Beautiful because they're all blocking the light right.
Speaker 3: Because the orbits are so tight and the planets are
Speaker 3: moving so fast, you don't just see one simple neat
Speaker 3: planetary transit at a time. You regularly have situations where
Speaker 3: two or even three planets are passing in front of
Speaker 3: the host stars simultaneously.
Speaker 2: Which compounds the starlight dimming. You get a shadow within
Speaker 2: a shadow.
Speaker 3: Precisely, the light drops when Planet A enters the disk. Then,
Speaker 3: while Planet A is still crossing, Planet B enters and
Speaker 3: the light drops further.
Speaker 2: Like stair steps.
Speaker 3: Exactly, Then Planet A leaves and the light goes back
Speaker 3: up a bit, but Planet B is still there. The
Speaker 3: shadow deepens and lightens in these complex overlapping asymmetrical steps,
Speaker 3: untangling a light curve like that. Trying to isolate the
Speaker 3: distinct mathematical shadow of three different planets overlapping each other
Speaker 3: while accounting for the gravitational tugs they exert on each
Speaker 3: other that slightly alter their transit times is a monumental
Speaker 3: mathematical puzzle.
Speaker 2: It's like listening to a massive symphony orchestra playing a
Speaker 2: complex piece of music and being asked to perfectly isolate
Speaker 2: and transcribe the notes of three specific violins playing at
Speaker 2: the exact same time while the drummer is occasionally hitting
Speaker 2: the microphone stand.
Speaker 3: That is a very accurate way to describe the noise.
Speaker 2: A human brain struggles to separate those signals. But it's
Speaker 2: exactly the kind of complex overlapping pattern recognition that a
Speaker 2: well trained neural network like Raven is built to decode.
Speaker 2: It looks at the tangled mess of shadows and instantly
Speaker 2: identifies the individual players.
Speaker 3: And discovering these compact multiplanet systems is vital because they
Speaker 3: are highly unstable on a cosmic timescale. The gravitational interactions
Speaker 3: in the system pack that tightly are immense.
Speaker 2: So they're tugging on each other constantly.
Speaker 3: Yeah. By cataloging them, we can study orbital resonances, which
Speaker 3: is how planets sync up their orbits to avoid colliding,
Speaker 3: and we can test our theories on planetary migration.
Speaker 2: But amidst all this crowded, chaotic orbital traffic, amidst the
Speaker 2: lava worlds and the pack systems, the researchers also use
Speaker 2: raven to explore something entirely different. They use this incredible
Speaker 2: tool to look at a very specific and surprisingly empty
Speaker 2: region of space. I was fascinated by this part of
Speaker 2: the study. The AI helped clarify the boundaries of a
Speaker 2: rare class of planets found in an area known as
Speaker 2: the Neptunian Desert.
Speaker 3: The Neptuny Desert is one of the most intriguing and
Speaker 3: hotly debated concepts in modern astrophysics. Essentially, the AI mapped
Speaker 3: out the demographics of planets and confirmed a region where
Speaker 3: theoretical physics strongly predicts planets of a certain size and
Speaker 3: composition should be almost entirely non existent.
Speaker 2: Okay, this raises an important question, why is it a desert.
Speaker 2: If we are finding planets packed in everywhere else, if
Speaker 2: we are finding planets hugging their stars on sixteen hour
Speaker 2: orbits and magma oceans, why does physics say this specific zone,
Speaker 2: for this specific type of planet should be a barren wasteland.
Speaker 3: It has to do with the dell violent balance between
Speaker 3: a planet's physical size, its internal composition, and its proximity
Speaker 3: to the intense radiation of its host star.
Speaker 2: Okay, break that down for me to break down.
Speaker 3: When we talk about Neptunian planets or sub Neptunes, we
Speaker 3: mean worlds roughly the size of our own ice giants
Speaker 3: Uranus and Neptune. These are planets that are significantly larger
Speaker 3: than Earth, but not quite as massive as Jupiter.
Speaker 2: And what are they made of?
Speaker 3: Crucially, they are composed of a relatively small rocky or
Speaker 3: icy core surrounded by a massive thick envelope of hydrogen
Speaker 3: and helium gas.
Speaker 2: So they are basically a giant, dense ball of gas
Speaker 2: and ice with a little pit in the center.
Speaker 3: Think of a peach, where the pit is the rocky
Speaker 3: core and the massive amount of flesh around it is
Speaker 3: the gas atmosphere. Now, if a planet like that forms
Speaker 3: further out in the Solar System. It's fine, it's coal,
Speaker 3: the gas is stable. But if that Neptunian planet migrates
Speaker 3: too close to its host star, say it gets dragged
Speaker 3: into that sixteen day or less orbital zone we were
Speaker 3: talking about earlier, it enters a profoundly hostile environment. It
Speaker 3: gets cooked, it is subjected to intense, searing stellar radiation.
Speaker 3: We are talking about a relentless bombardment of X rays,
Speaker 3: extreme ultraviolet light, and powerful stellar winds constantly blasting the
Speaker 3: planet's upper atmosphere.
Speaker 2: Okay, but a planet like Jupiter is also mostly gas,
Speaker 2: and we find hot jupiters orbiting incredibly close to their
Speaker 2: stars all the time. Why doesn' Jupiter get destroyed?
Speaker 3: That is the crucial distinction. For a massive gas giant
Speaker 3: the size of Jupiter, its own internal gravity is immense.
Speaker 3: The gravitational pull of the planet is so strong that
Speaker 3: it can hold on to its massive gaseous atmosphere despite
Speaker 3: the intense radiation trying to blast it away. It's too
Speaker 3: heavy to be blown apart easily got it. On the
Speaker 3: other end of the spectrum. For a small, dense, rocky
Speaker 3: planet like Earth or Venus or one of those lava
Speaker 3: worlds we discussed, there isn't a massive thick envelope of
Speaker 3: hydrogen gas to strip away in the first place. It's
Speaker 3: just a solid rock.
Speaker 2: Ah, So a neptune sized planet is caught in the
Speaker 2: tragic metal ground.
Speaker 3: It's the worst of worlds precisely. Its gravity isn't strong
Speaker 3: enough to tightly hold onto that massive, fluffy gaseous envelope
Speaker 3: against the relentless assault of the star's high energy photons.
Speaker 3: But it has too much gas to just ignore the radiation.
Speaker 2: So what happens to the gas?
Speaker 3: Theoretical models, specifically mechanisms like photoevaporation, where the light literally
Speaker 3: heats the gas until it escapes the planet's gravity, predict
Speaker 3: that the intense radiation should literally boil the atmosphere off
Speaker 3: into space.
Speaker 2: Wow.
Speaker 3: Over a span of millions or billions of years, the
Speaker 3: Neptune is violently dismantled. The gas is stripped away entirely,
Speaker 3: leaving behind nothing but its bare much smaller rocky core.
Speaker 2: So the peach gets entirely eaten away by the star,
Speaker 2: leaving only the pit, and that pit just looks like
Speaker 2: a slightly large, rocky planet. Therefore, according to the physics
Speaker 2: of photo evaporation. Finding an intact, fluffy, neptune sized planet
Speaker 2: with its atmosphere still attached that close to a star
Speaker 2: should be statistically impossible.
Speaker 3: It should be a desert, and yet we found some.
Speaker 3: Really Yeah, Raven's unparalleled precision allowed it to sift through
Speaker 3: the noise and validate a handful of worlds sitting right
Speaker 3: in the middle of this supposed wasteland. We are detecting
Speaker 3: intact neptunes surviving in a radiation environment that our current
Speaker 3: models say should have completely dismantled them eons.
Speaker 2: Again, which means our understanding of the universe is broken,
Speaker 2: or at least incomplete. If these planets exist where they shouldn't,
Speaker 2: there are survival mechanisms at play that we don't fully
Speaker 2: grasp yet.
Speaker 3: It forces the entire astrophysical community to reconsider the timeline
Speaker 3: and mechanics of planetary evolution. It poses massive questions like what,
Speaker 3: well are our models of photo evaporation too aggressive? Perhaps
Speaker 3: these neptunes only very recently arrived in their tight orbits.
Speaker 3: Maybe they migrated inward late in the system's life and
Speaker 3: the star just hasn't had enough time to strip them there.
Speaker 3: Yet we are catching them in the act of.
Speaker 2: Dying, or maybe the planets themselves have defenses we didn't anticipate.
Speaker 2: What if they have internal planetary magnetic fields that are
Speaker 2: orders of magnitude stronger than anything we've observed, acting like
Speaker 2: a mass of deflector shield protecting their atmospheres from the
Speaker 2: stellar wind.
Speaker 3: Exactly. These are the kinds of profound field altering discoveries
Speaker 3: that drive physics forward. When the data contradicts the theory,
Speaker 3: the theory must evolve finding these bizarre, extreme individual worlds,
Speaker 3: the lava planets, the crowded, chaotic systems, the impossible surviving
Speaker 3: neptunes is an incredible feat of engineering and software design.
Speaker 2: But when I was looking at the scope of the
Speaker 2: Warwick team's work, I realized that the true power of
Speaker 2: this AI isn't just acting as a cosmic stamp collector
Speaker 2: building a gallery of space oddities and weird planets. It's
Speaker 2: about doing planetary census work on a galactic scale. We
Speaker 2: are moving from studying individual interesting trees to mapping the
Speaker 2: demographics of the entire forest. Let's shift gears and look
Speaker 2: at the broader demographics of the galaxy. Because the numbers
Speaker 2: Raven generated are mind bending.
Speaker 3: This is where the Warwick Team's companion study, which was
Speaker 3: also publish recently, really changes the game for theorists.
Speaker 2: Yeah, let's get into those numbers.
Speaker 3: By having a data set that is so heavily vetted
Speaker 3: by Raven and so demonstrably free of those pesky false
Speaker 3: positives we discussed earlier, the researchers could confidently step back
Speaker 3: and perform massive demographic analysis. They mapped out the prevalence
Speaker 3: of these close in planets with unprecedented surgical detail. They
Speaker 3: weren't just asking what is out there? But how common
Speaker 3: is it?
Speaker 2: And the big demographic statistic they unveiled is staggering. It
Speaker 2: completely reorients your perspective of the galaxy. They found that
Speaker 2: around nine to ten percent of all Sun like stars
Speaker 2: host a close end planet, meaning a planet orbiting in
Speaker 2: less than sixteen days.
Speaker 3: Let's contextualize that number because it is massive. When we
Speaker 3: say some like stars, astronomers are specifically referring to what
Speaker 3: we call FG and K main sequence stars.
Speaker 2: Okay, what does that mean for a lay person?
Speaker 3: Stars are classified by their temperature and spectrum. F stars
Speaker 3: are a bit hotter and larger than our Sun. G
Speaker 3: stars are exactly like our Sun, and K stars are
Speaker 3: slightly cooler, smaller or dwarfs. These FGK stars make up
Speaker 3: a very significant, stable portion of the stellar population in
Speaker 3: the Milky Way. They are the prime real estate for
Speaker 3: finding planets and potentially life.
Speaker 2: Here's where it gets really interesting. I want you to
Speaker 2: go outside tonight, assuming it's a clear night wherever you
Speaker 2: are listening from, and just look up at the night sky.
Speaker 2: Pick out the stars you can see with your naked eye.
Speaker 2: The vast majority of the individual stars you can see
Speaker 2: are relatively close to us in the galactic neighborhood. Statistically speaking,
Speaker 2: roughly one in ten of the sunlike stars you were
Speaker 2: looking at right now has a planet whipping around it
Speaker 2: at breakneck speed, completing a year in less than sixteen days.
Speaker 2: It's hard to wrap your head around ten percent. It
Speaker 2: completely changes how you view the cosmos. The sky isn't
Speaker 2: just a collection of lonely isolated fusion reactors burning in
Speaker 2: the void. It is teeming with complex planetary systems operating
Speaker 2: on hyper speed. Every tenth star you point your finger
Speaker 2: at has a world practically touching its surface.
Speaker 3: It is a profoundly humbling to know that the night
Speaker 3: sky is that crowded. Now to be fair to the
Speaker 3: history of astronomy and to provide the proper context. This
Speaker 3: nine to ten percent figure isn't entirely new. It is
Speaker 3: largely consistent with the earlier estimates produced by NASA's legendary
Speaker 3: Kepler mission, which operated in the decade before tests. Kepler
Speaker 3: was the great pioneer of exo planet demographics. It stared
Speaker 3: at a single fixed patch of sky in the Sickness
Speaker 3: Constellation for years, and it gave humanity our first, real,
Speaker 3: mathematically sound estimates of planetary occurrence rates.
Speaker 2: Right. I'm glad you brought that up, because when I
Speaker 2: read the ten percent number, my first thought was, didn't
Speaker 2: we already know that from Kepler? If Kepler already gave
Speaker 2: us that ten percent estimate a decade ago, what makes
Speaker 2: the test data and the raven pipeline so groundbreaking today?
Speaker 2: Why is this a new breakthrough?
Speaker 3: It fundamentally comes down to precision and confidence. Kepler gave
Speaker 3: us the initial estimate, but it was an estimate with
Speaker 3: relatively wide aerror bars.
Speaker 2: So they weren't totally sure exactly.
Speaker 3: Yea, there was still a margin of uncertainty because of
Speaker 3: the false positive rates and the limitations of the older software.
Speaker 3: Raven processing the incredibly rich test data mapp this exact
Speaker 3: same demographic, but with uncertainties that are up to ten
Speaker 3: times smaller.
Speaker 2: Wow, ten times more precise. That isn't just a minor
Speaker 2: incremental update. That's a massive leap and resolution.
Speaker 3: Think of it this way. Kepler proved the concept. It
Speaker 3: was like an early explorer sailing across the ocean and
Speaker 3: sketching a rough pencil outline of a newly discovered continent.
Speaker 3: They could tell you the continent was there and roughly
Speaker 3: how big it was. But Raven and tests are refining
Speaker 3: that map to a degree we've never seen.
Speaker 2: They're mapping the actual terrain.
Speaker 3: They are like a modern constellation of satellites providing a
Speaker 3: high definition, millimeter accurate topographical map of that same continent.
Speaker 3: When your statistical uncertainty shrink by a massive factor of ten,
Speaker 3: your theoretical models of how solar systems form suddenly have
Speaker 3: to become much much more rigorous to fit the new data.
Speaker 3: You can't hide sloppy physics inside way airbars anymore.
Speaker 2: That makes perfect scent. The tighter the data, the harder
Speaker 2: it is for a bad theory to survive, And that
Speaker 2: exact precision is what allowed the Warwick researchers to finally
Speaker 2: quantify the Neptunian desert. We were just talking.
Speaker 3: About, yes, the desert numbers.
Speaker 2: We discuss the physics of why those planets are rare,
Speaker 2: how their atmospheres get boiled away. But the Raven pipeline
Speaker 2: allowed the researchers to move beyond just saying they are
Speaker 2: rare and actually provide the first direct, highly precise, empirical
Speaker 2: measurement of this phenomenon. They calculated that Neptunian planets surviving
Speaker 2: in tight orbits occur around a mere point zero eight
Speaker 2: percent of Sun like.
Speaker 3: Stars, less than one tenth of one percent. If we
Speaker 3: connect this to the bigger picture of astrophysics, that point
Speaker 3: zero eight percent isn't just a neat piece of trivia
Speaker 3: for a textbook. It is a hard, unyielding mathematical boundary.
Speaker 3: It is an empirical constraint on the fundamental physics of
Speaker 3: solar system formation and atmospheric escape.
Speaker 2: It's the universe drawing a literal line in the sand.
Speaker 2: It tells us, with absolute numerical precision, exactly where the
Speaker 2: tipping point is betwel in a planet surviving its stars,
Speaker 2: radiation and a planet being mercilessly boiled down to its bare,
Speaker 2: rocky core, and.
Speaker 3: By establishing these hard boundaries the ten percent prevalence of
Speaker 3: short period planets the point zero eight percent survival rate
Speaker 3: in the extreme radiation of the Neptunian desert, the Raven
Speaker 3: team is providing the anchor points for every single theoretical
Speaker 3: astrophysicist trying to write the history of the galaxy.
Speaker 2: So they have to build their models around these number.
Speaker 3: Exactly when a scientist today sits down to build a
Speaker 3: complex computer simulation of how dust and gas coalesced to
Speaker 3: form planets, they have to ensure that their simulation naturally
Speaker 3: produces these exact percentages. If your computer model of a
Speaker 3: solar system forms Neptunes in tight orbits five percent of
Speaker 3: the time, you know your model is wrong, because the
Speaker 3: Raven data says it only happens point zero eight percent
Speaker 3: of the time. It keeps our theoretical physics strictly grounded
Speaker 3: in observational reality.
Speaker 2: The extreme precision of these demographics fundamentally shifts how astronomers
Speaker 2: will plan their next decades of research, which brings us
Speaker 2: to the final and perhaps the most exciting part of
Speaker 2: this breakthrough, where we go from here because this paper
Speaker 2: isn't just the end of a study. It's not a
Speaker 2: closed book. It is the beginning of a whole new
Speaker 2: era of exploration. The Warwick team has embraced what I
Speaker 2: think is the absolute best part of modern science, the
Speaker 2: open source universe.
Speaker 3: It is a vital transformative point. The development of a
Speaker 3: pipeline like Raven doesn't just generate astronomical discoveries in a vacuum.
Speaker 3: It simultaneously stress tests the absolute limits of artificial intelligence
Speaker 3: on incredibly difficult, noisy, real world research problems.
Speaker 2: Oh that makes sense. A pushing AI forward to.
Speaker 3: Absolutely The complex neural network architectures and mathematical frameworks they're
Speaker 3: building to find exoplanets push the boundaries of machine learning
Speaker 3: in ways it could eventually benefit completely different, unrelated fields
Speaker 3: of science. The way Raven analyzes time series data to
Speaker 3: find a tiny drop in light could theoretically be adapted
Speaker 3: to look for microscopic anomalies in medical data or seismic waves.
Speaker 2: And crucially, the researchers haven't hoarded this AI or the
Speaker 2: massive catalogs of data produced. They didn't lock it behind
Speaker 2: a patent or a private university server. The team at
Speaker 2: WARG has released interactive tools, the underlying code and the
Speaker 2: full comprehensive catalogs of there one hundred and eighteen validated planets,
Speaker 2: and there are thousands of high quality candidates to the
Speaker 2: global scientific community.
Speaker 3: It's a huge gift to the field.
Speaker 2: They've essentially built a highly accurate AI verified roadmap of
Speaker 2: the local galaxy and they've just handed the keys to
Speaker 2: anyone on Earth with a telescope and an Internet connection.
Speaker 3: And that roadmap is absolutely critical for the next generation
Speaker 3: of space observation because the telescopes that we are building
Speaker 3: now are marvels of engineering, but their time is precious.
Speaker 3: We have massive new missions on the immediate horizon, like
Speaker 3: the PLATO mission right yes, For example, the European Space
Speaker 3: Agency is preparing for the PLATO mission, which stands for
Speaker 3: Planetary Transits and Oscillations of Stars. It is set to
Speaker 3: launch in the near field future, and PLATO is a
Speaker 3: massive step up. It is specifically designed to search for
Speaker 3: potentially habitable terrestrial Earth like planets orbiting in the habitable
Speaker 3: zones of Sun like stars.
Speaker 2: But launching a space telescope costs billions of dollars and
Speaker 2: observing time is incredibly competitive. And expensive. You can't just
Speaker 2: point Plato blindly at the sky and hope for the best,
Speaker 2: and you certainly can't waste its valuable time looking at
Speaker 2: targets that turn out to be false positive grazing binaries.
Speaker 3: Exactly, you need a rigorously vetted target list. You need
Speaker 3: to know exactly where to point the multi billion dollar instrument.
Speaker 3: The one hundred and eighteen planets dalinated by Raven and
Speaker 3: the thousands of pristine, high quality candidates that identified become
Speaker 3: that ultimate target.
Speaker 2: List, so they know exactly where to look.
Speaker 3: Ground based observatory is and upcoming space missions like Plato
Speaker 3: will use this exact catalog generated by the AI. They
Speaker 3: will know precisely which stars warrant the intense expensive follow
Speaker 3: up required to measure the exact mass of a planet
Speaker 3: or to use spectroscopy to look for the chemical signatures
Speaker 3: of water, oxygen, or methane in an alien atmosphere. Raven
Speaker 3: has cleared the brush so Plato can see the forest.
Speaker 2: So what does this all mean When we take a
Speaker 2: step back from the convolutional neural networks, the sixteen day orbits,
Speaker 2: and the Neptunian deserts and look at the whole picture,
Speaker 2: we are seeing a profound evolution in how humanity interacts
Speaker 2: with the cosmos. Artificial intelligence has allowed us to take
Speaker 2: the raw, chaotic, impossibly noisy light from two point two
Speaker 2: million distant stars and to still it into a high fidelity,
Speaker 2: highly precise map of alien worlds.
Speaker 3: It's beautiful, really.
Speaker 2: We've automated the discovery of the universe. We have outsourced
Speaker 2: our sense of wonder to an algorithm, and the algorithm
Speaker 2: is showing us things we never could have found on
Speaker 2: our own.
Speaker 3: It is the perfect synthesis of human ingenuity and machine capability.
Speaker 3: We built the magnificent telescope to gather the agent light,
Speaker 3: We built the intricate mathematical simulation to teach the machine
Speaker 3: the underlying physics of reality, and the machine tirelessly processed
Speaker 3: the sheer crushing volume with data that our biological brains
Speaker 3: never could have handled alone. Is a partnership that expands
Speaker 3: our reach into the dark.
Speaker 2: It makes you wonder about the sheer volume of information
Speaker 2: we've already collected across all fields of science, which leaves
Speaker 2: you with a lingering question to ponder long after we
Speaker 2: finish today. If a newly designed AI pipeline train purely
Speaker 2: on simulated mathematical realities can uncover over one hundred hidden
Speaker 2: exotic worlds hiding in plain sight within a four year
Speaker 2: old data set from tests. While what other fundamental earth
Speaker 2: shattering truths of the universe are currently sitting quietly in
Speaker 2: our existing data archives right here on Earth, just waiting
Speaker 2: for the right algorithm to finally open our eyes to them.
Speaker 3: The data is already there, resting on our hard drives.
Speaker 3: The universe has already sent the message. We just need
Speaker 3: to learn how to build the right machine to read it.
Speaker 2: It's exactly like looking for the impossibly faint shadow of
Speaker 2: a moth against a distant street light hundreds of light
Speaker 2: years away. The shadow is always there. Sometimes you just
Speaker 2: need a machine to tell you exactly how to look.
Speaker 2: Usai