Quasar Quirks & Sky Surveys with Matt O’Dowd

This is what life sounds like in Hertfordshire. This is what a life being saved sounds like in Hertfordshire. At Essex and Hart's Air Ambulance, we rely entirely on your generosity to keep us flying. Every day we react within seconds to bring life-saving care to those in our county who need it most. Please donate what you can today and help save a life. Search Essex and Hart's Air Ambulance. Chuck, I'd love having Matt O'Dowd back on. Catch us up on Quasars, on the Vera Rubin Telescope, on Big Data and AI. Yeah, and I found out that Vera Rubin is actually not a sandwich. Coming up on StarTalk. StarTalk begins right now. This is StarTalk. Neil DeGrasse Tyson, your personal astrophysicist, got with me Chuck E. Nice. Chuck, maybe? What's happening? How you doing? Doing well. You know what edition of StarTalk this is? Which one would that be? The Matt O'Dowd edition. Oh, well that's always good. Matt O'Dowd, welcome back to StarTalk. Such a pleasure to be here. It's my favorite subject. Oh, no, yes, it would be. I got you an associate professor up at CUNY, Lehman College. Exactly. Yeah, and that's right across the street from my high school. The Bronx High School of Science, don't I know that? Oh, wow. It was, yeah. Oh, yeah. Is that a feeder school too? I think the Bronx High School of Science go to straight to the Ivy League. They go straight to the Ivy League, huh? Yeah, or... Oh, well. But some of them do... That's CUNY's loss. Some of them come and hang out. Yeah, it's a good option. We've been home that way, yeah. Okay. And you're a research associate here at the museum. Exactly. Yes, and you're a host and writer of one of just the coolest YouTube channels, just PBS Space Time. Nice. I just so appreciate the work you put into what's on it, how you deliver it, and you're just so casually smart. Casually, but I work very hard to be a P.B.Casual. I know, I just, this is what I'm saying. The effort I see and I feel the effort, all behind you just being casually smart in those videos. It's like those $800 Rockstar haircuts. To make it look like you just went out of bed. But you spend 12 hours at the salon, exactly. That is the perfect metaphor for PBS Space Time. So, you have a research specialty in quasars, if memory serves. Is that correct? Quasars are a star, no pun, of my research. One or two things involved, but yes, the quasar is the coolest thing in space. I cannot argue with that. And also, lately you've been into AI and machine learning? AI and machine learning, exactly. I found that regular eye was insufficient for my needs. Regular eye. Turning to the artificial. Yeah, I got regular eye. That's where I am. You have sub-regular eyes, sir. Add about the Verubin telescope, which had first light last year, I think, didn't it? First light, they kept pushing it. It was actually just a few months ago, the official first light. So, it was a little more recent. So, it's in the calibration mode at the moment. The survey has not yet started. Okay, so the official data taking survey, excellent. Okay, so welcome back. Now, tell me, quasars, remind us what that is an acronym of? Wow, okay, so, and there's a whole history. No, I'm just going to say it's quasi-stellar radio source. Okay. The etymology is disputed, but quasi-stellar means, so like a star, as in a little pinprick of light on the sky, faint far away, only see it with the telescope. So, observationally, it's like a star. Exactly. Stars are not pinpricks of anything. Indeed. If you'd traveled to one as you have, Neil, then you would know that it's a giant ball of fusion. The quasar is something very different to that, but to us, us mere earthlings, looking out, we see these pinpricks of light with telescopes. You can't see them with the naked eye. Some of them also glare radio emissions, so they have these jets, all this cool stuff. And so, when these things were first discovered, there were these pinpricks of light on the sky that were associated with these confusingly loud radio blobs. And so, quasi-stellar radio source was a bit of a mouthful. Interesting. What's the latest on quasars? Because when I was coming up through graduate school, they were frontier. We were still figuring out, we didn't have the black hole model in place yet. It was contested, not badly contested, but it was, do we really need a black hole? There's got to be some other way. And they're all far away. How come there's none nearby? So, catch us up on quasars. All right. Let me catch you up on quasars. So, you know, there were the pinpricks, there was the radio, the, like the big, you know, the watershed moment was realizing that they were far away when we first took their spectra. We could see that they were moving away from us very quickly, explained by the expanding universe, but they have to be very far away for that. And at those distances, even though they look faint to us, they're insanely bright. So, once you calculate how much light there really is, maybe a thousand times the light of an entire galaxy from a point in, right in the middle of a galaxy. And so, you know, people came up with all sorts of swarms of neutron stars, you know, supernova, you know, storms. But the black hole. And at any time we're on the frontier, we don't know what's going on. Right. That opens the floodgates for theorists. Of course. Yeah, yeah, yeah. So many papers. To get a lot of energy out of a very condensed region of space, you know, the black hole is a good way to do it because there's no way to fit so much in it. Yeah, but if black holes suck, how are you getting energy out of it? That's true. That's true. Stuff gets in, but it doesn't all get in. I mean, so the, like, let me give you a painted picture of the quasar. So you've got a galaxy with what we call a supermassive black hole, a million to a billion times the mass of the sun, huge, gigantic ball of nothing, and gravity. It's a thing of gravity. And so when something happens to drive material too close to it, when the Milky Way has one, it's quiet, it's almost invisible. Sometimes they're not invisible. When something happens to drive a bunch of gas, stars, etc., then you end up with this screaming vortex of material pouring into the ball. You mean a mechanism to move stars, gas, and other material from wherever its orbit is down into the center. Yeah, from the galaxy, yeah. It has to drop in somehow. Yeah, like galaxy collisions or like, you know, close, you know, there are ways to do it. Because it wouldn't otherwise have an excuse to be there. You have to find a way. Something else to move it, right? If somebody's got to take it out of its orbit. Yeah, just like Earth doesn't fall into the sun, you know, like that. Gas doesn't fall into the center of the galaxy like that. It has to be perturbed. Okay, so you have a mechanism. It seems to me that would have been the challenge. How do you get a black hole to release these copious amounts of energy? Yeah, I mean, it's the energy of falling. And this gas falls a long, long, long way. Can you elaborate on that? The energy of falling. We did a whole thing on this. You get in an elevator, it takes you to a top floor, and then you store up all the energy, there's potential, and then when you drop, that's energy release. That's the energy that's falling. Is that what you're talking about? Yes. Hydroelectric dams work by the energy of falling. Okay. The water falls on a turbine. I get it, because the black hole has gravity, so there you go. I got it now, never mind. So the falling, the gas ends up moving at really insane speeds. Forms a whirlpool because things form whirlpools and tries to get in, but the black hole itself is this incredible choke point. It's like trying to cram a galaxy worth of gas into this little point. And so it screams into this black hole, heats up by friction at these speeds, and that friction liberates, so mass is energy, et cetera, and mass holds a ton of energy, hence nuclear power being so powerful. We liberate something like 10% of the rest mass of this in-falling gas is just pure energy in the form of light photons. Wow. And so they shine out, some of the gas gets in. But just to bring closure to this elevator with you on the rooftop, that energy is recovered if you jump, and it becomes kinetic energy. However, now it's just kinetic energy. How do you turn it into light? That now take me from there. Wow. So you have fast moving gas, something has to now eat that kinetic energy and turn it into light. So the simplest answer is it's hot. It's searing, so it's thermal energy in the end. You've got this whirlpool, the gas rubbing against itself, and it reaches these insane temperatures, so that right in the middle, your heater is infrared hot, the sun is visible, light hot. At the center, this stuff is x-ray hot. The temperatures are insane. But it's also violent. I mean, it's vortex of crazy gas pulling into black holes. So you've got these fits and bursts and energy blasting outwards. And I'm old enough to remember the first x-ray telescopes. We were excited because if they found x-rays being emitted from a place where, well, we don't know what else is happening there, there must be a black hole and the gas got so hot, it's now glowing in x-rays. Exactly. And we see those inside our galaxy also on a much smaller scale. The x-ray binaries, which are black holes that are eating their companion style. So very cannibalistic. So we have agreement on this model. Correct. I mean, the evidence is in, I think. We've now built telescopes that are good enough that we can, for more nearby ones, we can see the gas in that whirlpool and we can measure its velocities and we can say, well, in order for those velocities, there needs to be this gravitational field and literally nothing but a black hole can produce that gravitational field. Got it. So is it safe to say that a quasar, I think this is correct, but I've been out of it for so long and I just want to get updated, a quasar is like any other galaxy except its black hole in its center is having dinner. Its black hole is in its feeding phase. So the Milky Way black hole has been in that phase before. It's already been down that road. Maybe more than once. So could it be that a galaxy at the edge of our observable universe sees us at the beginning of the universe? Because that's the light only just now reaching them. Right. And they're seeing our supermassive black hole dining upon gases. And could we be a quasar to them? With one exception, I can almost guarantee that the exception is nomenclature. Quasar is for the brightest ones. The Milky Way would have been a different class of active galactic nucleus. Okay, got it. But a quasar like object for sure. Okay. And why would it be so different because of the size? It's not the size, man. Yeah, it's the size. The Milky Way's black hole at only four million suns in mass is piddling for a supermassive black hole. But quasars have more like a billion. So they're the big buzz. Damn. Okay, so how about M87, the big elliptical galaxy in the Virgo cluster. That's a hunk of black hole right there in its center. Exactly. And how massive is that? Is it a billion as around that? Okay, so that's a galaxy that might have been a quasar. And it was. And it's still an active nucleus, but it doesn't have the size of what we call the accretion disk, the whirlpool that it probably once had. But we can still see the jet. So there's like some magnetic fields shooting at this group. Isn't that the black hole that the telescope imaged? Exactly. What do we call it? The event horizon telescope. That's the black hole. So I'm talking about, oh, we can see the velocities of the gas. No, we have a picture of a black hole now. Yeah. Nice. Yes. That was banner headlines when it came out. Yeah. And a tremendous collaboration around the world. A collaboration of people, but also of telescopes. Yes. Because there were radio telescopes literally across the planet that stitched together their data to get the resolution needed to see that. Very cool. Wild stuff. And this is. Do you know about the data transfer process for the event horizon telescope? No. Planes. Okay. There was too much data for them to see over the cable. To go over the net. So they had to put them in boxes on planes and send them and stitch it together. Okay. So this is the bandwidth. The internet bandwidth of fax machines available. Well, exactly. Exactly. I mean, we're going to talk about big data. This is an example of. Yeah. I mean, in my day, I'd be at the telescope and the worldwide web was in its infancy and it wasn't even in public yet. I mean, just we had, we had our own channels to get to, to move data. And there's a point where we had to compare the data rate transferred from our telescope to our office versus FedEx. Yeah. No, I. FedEx and we load the tape on the other side. I traveled back from Chile a couple of times with the bag full of tapes of data. You got to think about, oh, make sure they don't go through the X-ray machine and stuff like that. Otherwise it's gone. Nowadays it just goes to the archive. Where you send somebody, you know, the data over the wires and then it gets there and they're like, it's okay. Timothy showed up. He walked here and bought us the same information. The bandwidth of Timothy. Imagine this, an alien spacecraft lands, an alien comes out and you are the first human it ever meets. What do you do? What do you say? In my latest book, Take Me to Your Leader, I offer a guide to how to survive that first alien contact. Not only scientifically, technologically, culturally and even socially. Not only for yourself, but in that moment you are a representative of the entire human species. You want to leave a good impression. Thank you all so much for being here at our wedding. I can't believe I get to spend the rest of my life with a woman of my dreams. Speaking of dreams, have you ever dreamed of tasting all the colours of the rainbow? Because that is exactly what you get with Skittles. Five bold fruit flavours in every pack. Lemon, orange, lime, strawberry and blackcurrant. They're chewy, they're colourful, they're perfect. Just like my wife. So thank you for coming and remember to buy Skittles. Shamelessly promote the rainbow. Taste the rainbow. So tell me what role gravitational lenses have played in this. Again, I'm old enough to remember the first lenses shown where one had to demonstrate that this curvy little image of a galaxy was the exact spectrum of a curvy image of a galaxy on the other side. The image had been split coming around a source of gravity. And we were partying to this because just what Einstein had predicted. And so now you guys have taken this to the next level. But I think before you do that, you might want to say exactly what gravity is. Even though you just gave, like, if somebody knows what it is, but it's kind of confusing when I first read about gravitational lensing because I'm thinking of actual lenses and looking through like... This is how you do it. You say, OK, OK, Mr. PBS space-time. Explain that. You want me to explain stuff? No. So in Einstein's universe, gravity mass bends the paths, it bends the fabric of space and that changes the path of light. It takes the shortest possible path, which is now curved. And so when a beam of light passes by a strong gravitational field, it arcs around that field. This is how Einstein's theory was first validated after Eddington goes to see an eclipse. He finds that the positions of the stars behind the... The solar eclipse. The solar eclipse, exactly. To see the night sky behind the sun and the stars have moved because of the sun's gravitational field. And we see this everywhere now when we look into the distant universe. We see giant clusters of galaxies and the galaxies behind them have been stretched out like this Funhaus mirror of gravity bending their light. And also multiply image. So you can see the same galaxy at the bottom of the cluster as at the top of the cluster, which you can verify through the spectrum. In the case of quasars, it's particularly cool because the most often scenario is a very distant quasar, a more nearby galaxy. And the light from that quasar will take two or maybe four different paths around that galaxy. And so to us, we only know where the light comes from. So it looks like there are two or four images of the same quasar. Was that called the Einstein Cross? The most famous one is called the Einstein Cross. Yeah, because it was a quadruply lens object. Exactly. One of the early ones that was found. And so what you're saying is there's four pictures and they're the same because it's the same source is making the four pictures. One key difference is that they're often at different times because the path lengths are different. So you're seeing the same quasar with offsets of somewhere between hours and weeks. And that's crazy powerful. Wait, hold on. You only know that if something happened on the quasar that you can start to talk. If it's just a static image, you wouldn't know. Exactly. That's fantastic. Isn't it? I mean, that's just freaking fantastic. You can predict. You can say, oh, this burped over here. Watch this. Wait for it. Wait for it. That's amazing. Wait for it. I feel like you have like a tiny little reset time machine that you're able to observe what's happening. You know, let me see that again. They call gravitational lenses because it's gravity kind of acting as this galaxy size lens in addition to your telescope. It's a crappy lens because it's made up of stars and you know, it's not very well ground. And so what you see is a little messy. Right. You see magnification. Yes. Very helpful. But you also see new fluctuations in the quasar due to the fact that the galaxy itself is made of stars and the stars are moving compared to the galaxy. And so you see all these new effects of a kind of crappy lens. But if you can model all of it, then you can use the gravitational lens to actually map the inner regions of the quasar, which is still very hard because they're still very tiny and very far away. God, wait a minute. You're saying the lens gives you access, deeper access to the quasar than you would otherwise have with just an image of a quasar. Very much. So it's a telescope booster. It's a telescope booster. Oh, nice. Very good, Chuck. Yeah. Cool, man. It's not a... Or enhancer, I would say. Yeah. I mean, you got to do a little bit of work. You know, it's like when they first put the Hubble mirror up and it was ground wrong and they had to do a lot of work to recover the images. Likewise, these crappy lenses. The same word, ground. You mean when you take the shape of the mirror. The geometry needs to get... Wait, does it happen? Oh, God. You don't remember that? They had the wrong shape. Where were you? Where was I? I was watching Japanese anime. That's where I was. Hubble had the wrong shape. By the way, it had the wrong shape, but it was perfectly ground to the wrong shape. So rather than replace the mirror, we took the other one. We took the other optics and compensated for that other shape and then put it right back in the business. Like basic surgery. Okay. Yeah. Okay. The reason why that happened, not to get so off topic, is the mirror was tested in situ and it had a perfect shape. Okay. And the other lenses were tested in situ, but they were not tested together. Together. Okay. Until it was too late. Yeah, still a dumbass mistake. Okay. So... What's great. Does that mean now, the farther away the quasar, the more likely you'll have lensing opportunities for things to be in your line of sight? Why yes, that would make sense. But now it's farther away, but now it's dimmer. Yeah. So there's a trade-off. There's a perfect configuration of distance, so the lens distance to the quasar. And so there's some factors, but it's true that very nearby ones are very unlikely to be lensed. Because there's less stuff, less chance of stuff being between you and it. Yeah, exactly. So nearby would be how far away? Oh, is it... Nearest town New York. No, it's... Nearest quasars have far out of those. I mean, the nearest ones are, you know, so M51 is one of the newer ones. Let me take it off of that. When I see maps of the large scale structure of the universe, the quasars are the most distant objects in these maps. Is that because they are the most distant? Or is it because they're the only things you can see that far away? But other galaxies, ordinary galaxies are populated among them and you just can't see them. Yes. Okay. No, let me... So yes, yes, and one more thing. So first of all, there was a quasar epoch when quasars were... The brightest quasars were the most common. And this is like the middle third of the age of the universe, basically. And we're now post that. There are still some big ones locally. The other thing is that the brightest quasars are quite rare. And so you just buy statistics, you have to look a long way to see the first one, right? Okay. Okay, so if you're in a sparse forest, the nearest tree is likely far away. And lastly, they are the things that we see to the greatest distances. So when you see these surveys and we can't see the galaxies, we see these pinpoints of light. Oh, there's a galaxy there, but we see it because of the quasar. Right, and you can't see the galaxy because it's not as bright as the quasars. Exactly. Okay. But to our galaxies, we wouldn't otherwise have seen were it not for the fact that they were far away and lensed. That's also true. Yeah. Yes. We see galaxies far away now. So you're getting a real assist from the universe itself. Yes. Yeah, no, that's... Lensing is very powerful. That's crazy. It's the best. I predicted by Einstein. Another crumb that just fell off Einstein. And he wasn't even thinking about it. He wasn't even thinking about it. He was thinking about what there might be some gravitational lensing. That's exactly how that... Who cares? That is exactly how that went down. Wow. No, Matt, you notice how that went down. He actually didn't think we would ever observe gravitational lensing out in space. He thought it would be too weak and too far away and... Oh, I think too rare because he was only thinking of it in an exact alignment of two objects. Okay. And then you get an Einstein ring. Right. And then it just falls off, then it splits the image. It splits the image. But if it's exactly on it, then the light is equally likely in any direction coming around it, and you get a ring. I think he figured an exact lineup was rare. But also in 1915, when General Relativity was published, we didn't even know that the universe existed outside the Milky Way Galaxy. Very good point. So maybe Einstein did, but he wasn't telling us. No, no, no. Hubble, Hubble. 15 years later, Hubble. Right, Hubble came along and it was just like, yeah, there's a lot more. Yeah. All right, so thanks for catching me up on quasars. So these sound like complicated problems that need more than just I to solve. Well, I spent my research life staring at these things with my human eyes, but until recently it's been possible. When I started out, we had maybe a hundred of these lensed quasars, then 300, but we're about to find many thousands. This comes to you from surveys. It's all going to change with Ruben, which will discover many thousands of these lensed quasars. Okay. Countless other things. So is normal eye, normal intelligence not good enough to handle this problem? It's barely good enough to do it even when you have one, because the systems are so complicated. You can't model it easily. You've got these stars in the galaxy moving around. You don't know where the stars are. You have to do that kind of statistically. Within the quasar. Stars moving around within the quasar. Stars moving around within the lensing galaxy that changes the way the lensing works. Oh. It's very messy. But it's powerful because those stars, they sweep across the inner structure of the quasar like this kind of radar. Vroom, vroom. And they can map it. They can map it at the same resolution as the Event Horizon Telescope, the thousands. So I can I misunderstood. You're saying the galaxies that are lensing the quasar, the movement of stars within those galaxies, give you varying patterns in the quasar itself. Exactly. Yeah. That's what you were saying. That's what I'm saying. Yeah. You have a quasar. You don't really know what's happening in there. Oh my gosh. Right. Right. Imagine this distant quasar. It's very small, but you've got this lens and you're kind of sweeping these complicated magnification bands across it. So you see different parts of the quasar change over different times. You can even see when it sweeps across the black hole in principle and see it darken for a little bit. All of this is going to be seen by Ruben. Wow. But. But. OK. Like P.B. Herman said, everyone has a big butt. OK. Go. So our big butt is big data. It's the fact that we need to now model thousands of these things and we could barely do one. And so we are indeed turning to AI machine learning. So not just AI intelligence, right? Yeah. Artificial intelligence. Artificial, exactly. Which presumes that the artificiality of it is better at it than you are. Yeah. I mean, it's such a catch all term and it's not, you know, we're not putting them into chat GBT where. No. Bill's family, right? Sophisticated neural networks of different types to do various of these. I mean, I don't want to sound glib, but it's kind of a matter of just pattern recognition at that point, right? You know what? Glib, that's exactly what it is. OK. These things can be very good at pattern recognition. Better than we are? They can find. How can they see Jesus in a piece of toast? If you train. No, but in tortillas you get them all the time. Oh, so tortillas. Jesus in a tortilla. Yeah. The Andrew is absolutely there, particularly good at that. But, you know, when we come to some data and we look for the patterns, we look for the patterns that we think are going to be there, right? Or the relationships between the parameters that we think are important with, you know, different types of AI model you can, you know, you can throw in the data and it will find patterns, even if they're patterns that weren't expected. But my question to you is, we are excellent pattern recognizing creatures so good at it that we will see patterns even that aren't there. Right. So is AI equally as susceptible? Is it that good that it's as bad as we are? That's a great sentence. That's a sentence I wanted to stay, but that's it. So there is that, and there's also the fact that it will find what it's expecting to find, just like we will. And we see what we expect to see. Do you remember, I mean, it's only a few years ago, but it's now like the ancient era of AI when you train these neural networks to recognize whether something's a cat or a dog. Yes. And you show it a cat, 100% of the time it knows it's a cat, a dog, it knows it's a dog. Right. If you show it a chipmunk, it will definitely say it's either a cat or a dog. That's a dog. Right? Yeah. Okay. So it'll see what, it all depends on how you train it. Just a chipmunk. Yeah. Okay. We trained on chipmunks. Okay. We trained on, so these are some of the challenges that it's as good as what you put in. Yeah. But if you are training it for what you expect, it's not going to find something that nobody expects. Unless. Which is serendipity on the frontier of science that we all cherish so highly. But what you could do if, I mean, I'm just spitballing here, but you could just allow it to find whatever pattern it wants. Like whatever pattern is there, just find me a pattern. You have to know what pattern means. But it already does. It can extrapolate from the patterns that you already trained it on. So then what you can do is. But then it's still extrapolating from a given pattern. So you can find a pattern for which there is no template. Oh, wow. That's the question. There you go. Okay. So can AI find a pattern on which it has not been trained? Yes. And that can be an authentic pattern, not something in its imagination. Yeah. Like when we find patterns that aren't there. Yeah. There is a power to, you know, in our case, it's putting the physics in and training it on what you think the physics is. That's actually quite a powerful approach. Like this sort of simulation based. It's foundational. Yeah. And it tells you what is happening in the context of what you put in. But what if you didn't know what was in there? So, but there are, so, you know, we talk about supervised learning and unsupervised learning and there are techniques for unsupervised learning where you, you didn't tell it anything. You just said, what do you see? What do you see? And then, you know, it'll tell you something and then it's your job to interpret what that means and why those patterns emerge. And has the answer ever come back a bunny rabbit? It's usually chipmunks, actually. Well done, sir. So is that precisely how you're using your AI? We do a bunch of things and, you know, Reuben and the other surveys do a bunch of things. In our case, it's, we use variational auto encoders to take these, in our case, it's the fluctuation over time of these light curves, compact them down into a much more compact space in, you know, what we call the latent space and then use that latent space to try to reproduce the data. In that latent space, we know that the patterns are hidden and so we have further neural networks to extract things like what is the mass of the black hole? How fast is it spinning? All of this good stuff because in principle, the network has learned what the fundamental parameters that went into generating those fluctuations were. Right, you need to know the physics of everything going on. If you miss some physics, you don't know what you're doing. Yeah, but you can expand the input physics well beyond what you think is in reality. And we try to do that. We try to say, all right, well, what is the span of all possible physics of, you know, of these quasars and lenses, et cetera, let's go much bigger than that to make sure we encompass the true space. And then you can also test how brittle it is. Like you can break it and see if it still gives you reasonable answers. So, you know, there's ways, there's ways around the chipmunk problem. You're checking the sensitivity of the system. Sensitivity and bristle-ness and see how you exactly. Okay. That's pretty wild, man. I mean, by the way, I want to say these are all done by my graduate students and postdocs. I, I come here and talk about it. Good for you. You're the Bahamas while they're doing this. Well, Bahamas did come into it, actually. No, let me, let's tell the story. I don't need to hear your Bahamas words. That's fine. If anything, I found just the smartest people to do this. And, and I mean, these days, you know, I learned programming on Fortran, right? As did I. I confess. Fortran for me. It's quite difficult for me to really grok the ins and outs of all of this. So I just try to. You an old fart. Oh yeah. You make a difference. You already did your time. I did my time. Yeah, you did your time. You did your time. I get to know wisely and creep-e-deen. Now you get to taste the nod, not breast-wreck and take credit for their work. That's how it goes. And one day they'll get to do that to the next generation. That's how it goes. You know what I mean? It's beautiful, really. So we've seen photos of the Verrubin telescope in the Andes Mountains of Chile, where we have a lot of telescopes. And there's the dome. And then there's this whole other section sticking out the side. The shoe, yeah. Oh, I didn't know. To me it always looked like a shoe. It looks like a shoe sticking out because it has a rounded front. I presume that's where all the data, the big data is happening. Yeah, I think they stacked the data in there. I think there's a lot of. And then they fly it up to whatever. There's a lot of onsite processing because you can't just ship it all. So is it because this telescope is uniquely in need of data processing support that it was conceived and designed this way? Well, there's also, you know, there are people there doing important engineering things that they haven't been replaced by AI like we have. So engineering support, but also the computing facilities that are there. And yeah, I've never been, I want to go and take a tour of the shoe. Yeah, the shoe. Yeah. So what it seems to me, I don't want to speak for you, but tell me if I'm correct, that most people's fear of AI is that it'll take their job. Whereas when you're a scientist on the frontier, such as yourself, the AI allows you to step where you could not have stepped at all. So it's not the same thing as replacing a job. It is empowering you to think more creatively about your thoughts on the scientific frontier. Is that a fair characterization? It's such early days in this revolution that it's hard to say where it's going to land. Right now it's insanely powerful. In many, many respects, it takes away work that, you know, we didn't want to be doing anyway. It's a lot of great work. It's incredibly powerful, but it's also, you know, the new reasoning models are able to do things that previously graduate students were doing. And the hope is, oh, well now graduate students can be freed up to do better things. Yeah, more creative things. That's the hope, but the reality might be that the AI is like, look at you dumbass. I can't believe you thought that this was something viable. God, who hired this dude? That's a little scary. Yeah, I mean, on the other hand, the stuff that graduate students used to have to do, which is stare at this boring data forever, you know, an AGI, artificial graduate student intelligence. Very good. That was nice. Do that. Do it at least in an afternoon, essentially. And so the hope is that the professors won't say, oh, I don't need graduate students anymore. They'll say, oh, graduate students, I don't get this stuff. Please do this. Now let me ask you this. Is there any benefit to the graduate student doing the grunt work? Does that, there's something that can come out of that for our brains? I'm going to say no. Here's why. Really? In my day, pre-AI, but computer power was growing exponentially. There used to be a course in graduate school on spherical trigonometry. Which nobody needs now. Because the computer does it. Exactly. Okay, spherical trigonometry. You know, trigonometry normally on a flat piece of paper, but on the dome, you have angles between stars and moving the telescopes. And what's the shortest slew path between two, that was, that's all spherical trigonometry. Gone. We just push a button. And it's done. The telescope calculates it. You see this, you took spherical, but you took it, right? No, no, no. It was like two years before I got there. We stopped, they stopped teaching it. And do you understand spherical trigonometry? No. That's the real question. I mean, the question is, are there intuitions that, that we miss now? I think there could be intuitions. Intuition things. Yeah. That's really what I'm talking about. Going through the wax on, wax off of graduate school. Oh, what were you learning? I don't know. I know Kung Fu. We don't know what we lose, I guess. Interesting. So, so I, okay, let me give a counter story to that. Okay. When I was in graduate school, a member of our department, a faculty member was world's expert on galaxy classification. Okay. Okay. World's expert. Okay. There's never been an expert such as him either before or since. Okay. Okay. And so I, in one of his classes, we are classifying galaxies. It's like, this is stupid. Why, why am I doing this? All right. Okay. And nowadays, computers classify galaxies. You don't need to do this. But when I look at a galaxy that you just took a picture of, I have a whole other relationship with it that you don't. I'm feeling it. I'm, because I'd looked at hundreds and hundreds of these, maybe thousands of these. And so it's in me in a different way. It's almost a muscle memory of what it is and why it looks that way and what I can couch. And to what I was saying earlier, who knows what inspiration that is inspired by just that reservoir of seemingly useless knowledge. Like you said, the wax on, wax off. Yeah. That's precisely the. And, you know, knowing what's under the hood, knowing how the sausage is made. You know, these days, future generations of graduate students won't know how to code because they talk to the computer. They vibe code. They'll code. They never make an if statement. And what do we lose? Maybe nothing. But I feel like there's something about knowing what's under the hood. That kind of helps. Well, that helps, you know, helps you know what the true capabilities are. You know how your smart phone works. No, you don't. Yeah, like you don't know what the vulnerabilities are. If if if it's just a black box, I guess it's a perfect black box. There are no vulnerability. Okay. So we just need to build that. Yeah. Hey, this is Kevin the Somalia. And I support StarTalk on Patreon. You're listening to StarTalk with Neil deGrasse Tyson. Let me just make closure to this data challenge that we now have. We are awash in data. The data rate for the Rubin telescope will exceed that of any previous telescope in our portfolio. Orders of magnitude. By orders of magnitude. Wow. I'm reminded of what was the first time they turned on the telescope and they discovered like a thousand new asteroids. Crossing the sky. Right. And you can do that because it's taking its repeated imagery, which is the only way you'll know if something moves. Yeah. Because otherwise it's a still spring. It's a static shot. It's a stock shot. And is that a star or is it in? Right. Is it something that goes bump in the night? Yeah. So that forced us, our field, our community to innovate in ways we didn't have to before. Right. So it was all good. It headed in the right direction there. Yeah. Do you mean as astronomers or as a conversation? All of the above. Oh, that's great. You were so incredulous. You're like, yeah. I offended. No, Neil. I'm wasting my life. It's funny because it's true. I only say that because we had needs for imaging the universe that exceeded what Kodak now many decades ago routinely produced. And so we, they put a special team on our needs and created special emulsions that were more sensitive, that were larger. And so when CCDs came out, we were the first to fully exploit them. Right. And before anyone even knew what they were. And poor Kodak did not take advantage of that. Kodak, what? It's just a flash in the pan. They're like, we're going to do this for these nerds. People love film. Yeah, these handcuffs need these great feets. They'll love film all the time. So only when it became a commodity did, were we no longer the leading edge of that. But it seems now with this level of data, is this more data than anyone has had to think about before? Are advertisers mining data off of social media accounts? And is that a greater repository of data for them to sell as a product? I mean, it's greater for them in terms of bits. I don't know if it's greater. So it's enormous. It's for the first time taking a full image of the entire southern sky every three nights for 10 years. I can give you some numbers about how much data that is. The CCD is enormous. It takes like 400 HD TVs to show one image. 400. 400. And that's one image and the southern sky is like 3,000, 4,000 of those images. Just to be clear, the Hubble telescope field of view is a fraction the size of the full moon. Wow. And this is like 40 times the size for one of those images. Of the full moon. That's amazing. So that's how you can... So if you said Hubble, give me an image of the whole sky. Okay, call me in 30 years when I finally... I'm gonna have to stitch this all together. I'm gonna mosaic this. Call me in three days. It's pretty fast. It's pretty fast for the whole sky. Wow, that's the very rule of the telescope. But like you said, it's a movie of the sky. So we see things changing, things going bump in the night. We see the quasars flickering at the edge of the universe. All of it. And it's all just gonna be like main lines for 10 years. And what do we do with it? Well, we work very hard. You guys are making like a flip book of the universe. It's a flip book of the universe. It really is. But it's a big flip book. It's a part of the pages. It's like several... To rub your thumb across the... You put the thumb across the page edges. Yeah, that's funny. I got a paper cut. Yeah, I lost my hand. Yeah. So just to anchor this in proper context at the risk of repeating myself. In my day, you go to the telescope, you take a picture, and you take the picture home and analyze it. If something moved, you would have no idea. You have no idea. And it was not that big a problem because stars live 10 billion years, 5 billion years, and you're there getting a 30-minute exposure of it. You're not expecting fireworks in that moment you're looking at it. But maybe there are somewhere in the universe. Well, there definitely are. We know there are. We know a lot of what the fireworks are, but there's a lot that we don't know also. The universe is pretty violent. It's quite dynamic. Yeah. Yeah. It's tough. So let's... It'd be a shame for us to sit this close to each other and not compare notes. So you've got a YouTube channel. You bring delivered content that's fun and interesting and exciting to hear. It's a mix of not only what is frontier science, but also it's fun science. So it's clear that you're doing some cherry picking of what you could be talking about. Plus, you look like the sexy professor that one might daydream about. Oh, thank you. The other thing that we go... So share with me some of your tools and tactics that you've found most potent in your efforts to bring the universe down to earth. So PBS Space Time feels a particular niche, I think, which is that we do go hard. We've covered the holographic universe and quantum mechanics. You've been out there. Yeah. All the way to the edge of the holographic universe, as well as more traditional space stuff. So we found very early that there's a huge appetite for kind of seeing under the hood, like how science happens, how scientists actually talk. And I think for the longest time, people have felt a little bit babied by a lot of popular science media. And they know when they're being talked down to. They know when they've been talked down to. And so I think one thing that I do well is I have a good jargon detector. I know when something's jargon. And jargon doesn't have to be like a specialized word. It can even be a specialized use of a word. And so the point is that so much of science, even the stuff that's hard, is accessible to human language and it can be talked about in human language. And I think that scientists are not the best people at doing that because they talk professionally. They talk in professional. You're not trained for that. It's all shop talk for scientists. It's all shop talk. Yeah, but all professions have shop talk. Exactly. Right. So what's different about science? Science is hyper, hyper specialized just because it's old. We know so much about the world that to make any progress, we have to dig deep and narrow to make any progress. And so the language around each subfield tends to be very specialized. And every time you get a subfield of a subfield of a subfield. Absolutely. It's excluding of others because of the language and because it's, you know, it attracts a certain type of people, nerds. They're really into. No, it's true. And they enjoy going granular on information. Yeah. And then science has become, I kind of think of it as genrefied. So, you know, something is sciencey if it's hyper focused and detail oriented when really it's about curiosity about the world. Right. So I think, I think the beginning of science and what it really is, is just a, it's curiosity with, you know, organization and, and this is where we are now after centuries and millennia of doing this. We are, we know a lot, right? But, but a side effect of that is the siloing of fields between each other and between scientists and non-scientists. And just the fact that the scientist and science feels like an other type of thing. It's a certain type of person who becomes a scientist and is into science. Whereas, you know, one thing we try to do is to make things sound a little more collective. You know, what, you know, I rarely say. Inclusive. Inclusive. Yeah. So, you know, we're very selective in the sense that, you know, yes, we should be very proud of Albert Einstein for coming up with general relativity, but we should be proud of humanity also for coming up with, you know, for one of us for figuring this stuff out. You know, we figured this out. And so that's, I think about that all the time. That's why I have an active disinterest in my genealogy. Because I want to be what I want to be based on what I know humans are capable of. Right. You know, somebody before you. Right. Right. But also it's lazy when people do that. It's completely lazy. Very lazy. Because what you're saying, it's like when people say, we did it! We won the world theory! Who's we? And I'm like, really? What? Now, where's your contract off? Yeah, yeah, yeah. Like, shut the hell up. No, no, but you're allowed to participate as a species in the achievements of your species. Yes. I think you're allowed to do that. Without a doubt, but the fact is that... You say we figured out how to build a suspension bridge and figure out how to go to the moon. Yes. And I don't want you coming behind me saying, well, what part of the project did you work on? Well, no, that's different though. Why? Here's why. Because it's my tax money that went in? Not even that. You did, your tax money did help build that. But the thing is that science is not so specialized like a sport or something else like that, that you can't do it. You can actually do it. If you want, you can understand and believe me, I'm speaking from experience, you can understand this stuff. It's a little difficult, takes a little bit of work, but once you do that, it's like, oh my God. But I think the public sense is the opposite. I think the public sense is that sport is more accessible than science is. And that's my point, but what my point is this, that's how you... Sport is not more accessible. Of course. You never hit a home run in a major league part. You give a damn what you think you can do, it's never going to happen. So it's like the person graduating high school saying, or college saying, I want to be a professional basketball player, let's say, and they're way more neurosurgeons or aerospace engineers. So that's too hard, I want to do this easy work. I want to do this easy work, and it's the hardest thing in the world, you're never going to do it. You see kids play basketball and you're not really into... So you're not genre-fied for liking basketball, you're a kid. Right. And you play it not because you might one day make the NBA, but because it's fun. But science doesn't have that nearly as much. Right, but guess what? The kids who do science, they're at the nerds. But you guys do science. I think you're not giving yourselves enough credit here. Okay? What you do is make science like basketball. And quite frankly, there's an audience out there that really feels that, and they appreciate it greatly. Well, thank you, Chuck. I feel like that is a big part of the goal. I think we have a lot of work to do. Can I tell you my transition? Thank you for saying that. That was insightful. Oh, cool. Listen, even a broken clock is right twice that. Okay, broken clock. So you got one more. That's great. Okay, so in my sort of rise in visibility, in the early days, people would come and say, are you Neil Tyson? I said, yes, I am. Okay, tell me about black holes and what you said the other day. So all I was was a food for them. There wasn't the fame was the fact that I had excited their curiosity. Okay. They didn't care what my favorite color was. As that might visibly got higher and higher, more and more people would just say, oh, can I get your autograph at pre-selfies? Can I get your autograph? And wouldn't it? Don't you want to ask me about the universe? No, I just want your autograph. So I felt cheap. When was the last time you told someone about black holes? You didn't want to? I mean, everyone you tell about black holes. So this continues and more and more people now they want to take selfies. That's fine. And I oblige. But I felt incomplete by this until someone told me, I said, Neil, do you realize you're a scientist and people want your autograph? Right. You're a scientist and people want to take a selfie with you like a rock star or athlete. And so I had to, because they're not going to go to the athlete and say, please explain your, they just want to, they want to be, they want to connect with the athlete. They just want to connect in that one way. Right. And so I realized, oh my gosh, the science is sharing the space that it only previously been occupied by. And it's happening more and more. By the way, I got it. Alex, our producer, you were not here. I've never said this to you, but I'll say it now because he was here and we were talking about you and I said, we were talking about you. And I said to Alex, do you know how hard it is to be world famous? Are you saying? Yeah. Yeah. Yeah. There might be just a few dozen people scientists who became it's what I'm saying. But what's great is I love what you said about the accessibility and not talking down and allowing people to find the wonder in science because I think that not only is it necessary for, for science to continue to thrive, you know, socially, but I think it's imperative for democracy, for our species, for the future of the, not just the country, but the world with respect to the things that information that we know that will save us as a species like climate, you know, and all these things really are very deeply connected to science and scientific literacy. And that's the truly important thing that you guys are doing. He's running for office soon. Well, you got my vote. But it's not just what science can do for us. It's this suite of modes of thinking and tools of thought and just ways to apply curiosity and rationality that are absolutely not restricted to what we now think of as science. It's just thinking. It's just thinking carefully. Absolutely. And it's so important in all ways. A state of mind, a state of thought, a state of curiosity. I love it. Yeah. The last thing I'll say here is I had to learn this after I stopped teaching classrooms and started writing books and doing podcasts. There's no obligation in a person's first encounter with your expertise, flim to learn everything on your syllabus, throw out the stuff that's boring or uninteresting. Yeah, it's got to be there in a classroom because you need the sequencing, but pick the stuff that's really cool. Teach them that. Then they say, this is cool. I want to learn more. And then they're at a level where they can get the nuances that you left out in the first place. Yeah, yeah. But also throwing in a few little deep views is nice also. Teases for the future. Oh, yeah, yeah, yeah, yeah. But I'm just saying often there's detail that's just simply unnecessary at the first pass. And it's hard to know how to spot that and exercise it. We're very precious about our details. Yeah, it is. It is. Dude, we got to end it there. Yes. Oh, yeah. Matt. This was fun. Thanks, Matt. Always good having Matt on the shelf. It's nice to come down to the fifth floor and get a chat about quasars. All right. Chuck, it was good to have you, man. Always a pleasure. And you're special. You're coming special. Still airing on our YouTube channel. YouTube channel. And it's called what? Chuck Nice, Just Smart Enough. Because I'm sitting between these two. So that's about as good as it's going to be for me. I love that. I think you're smarter than Just Smart Enough. But all right, this has been another installment of Star Talk, the Maddo Down edition. Yes. Until next time, keep looking up. Thank you.