Quasar Quirks & Sky Surveys with Matt O’Dowd

Summary

StarTalk Radio: “Quasar Quirks & Sky Surveys with Matt O’Dowd”

Airdate: June 9, 2026
Host: Neil deGrasse Tyson
Co-Host: Chuck Nice
Guest: Matt O’Dowd (Associate Professor, CUNY Lehman College; Host, PBS Space Time)

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Overview of the Episode

This episode dives deep into the cosmos, focusing on the enigmatic quasars, groundbreaking sky surveys like those from the Vera Rubin Observatory, and the evolving role of artificial intelligence in astronomy. Host Neil deGrasse Tyson and comic co-host Chuck Nice are joined by astrophysicist and popular science communicator Matt O’Dowd for a lively, jargon-busting conversation. Together, they unpack the mysteries of distant quasars, gravitational lensing, the promise of Rubin's unprecedented data, and how big data and AI are revolutionizing our ability to “see” and understand the universe.

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Key Discussion Points & Insights

1. What Are Quasars? (05:17 – 13:20)

• Definition & Etymology:
  • Quasar stands for “quasi stellar radio source,” describing bright, star-like points with strong radio emission.
    • "Quasi stellar means... in a little pinprick of light on the sky, faint, far away. Only see it with a telescope." – Matt O’Dowd (05:38)
• Nature and Energy Source:
  • Quasars are supermassive black holes at galactic centers, accreting vast amounts of matter, which rubs together, heats up, and emits immense radiation across the electromagnetic spectrum, especially X-rays.
  • The black hole “dines” when galactic material is funneled toward it—often due to collisions or interactions—resulting in stunning brightness (10% of infalling mass converted to energy).
    • "The energy is in the falling...this gas falls a long, long, long way." – Matt O’Dowd (09:09)
• Quasar Evolutionary Phases:
  • Our Milky Way's black hole is now “quiet” but could have been active in the past, visible across the universe as a quasar under the right conditions.

2. Gravitational Lensing — Nature’s Telescope (20:24 – 26:16)

• What’s Gravitational Lensing?:
  • Massive objects like galaxies bend light from objects behind them, often resulting in multiple (and time-shifted) images of the same object (like the famous Einstein Cross).
    • "They're called gravitational lenses because it's gravity kind of acting as this galaxy-sized lens in addition to your telescope." – Matt O’Dowd (23:41)
• Importance for Quasar Science:
  • Lens-induced multiple images arrive at Earth at staggered times, letting astronomers “replay” cosmic events.
    • "You can say, oh, this, this burped over here. Right? Watch this...wait for it." – Neil deGrasse Tyson & Chuck Nice (23:25)

3. Big Data in Astronomy & the Vera Rubin Observatory (29:17 – 38:10; 48:00 – 52:28)

• Data Deluge:

  • The upcoming Rubin Observatory sky survey will routinely generate data volumes orders of magnitude greater than any previous telescope.
    • "The data rate for the Rubin telescope will exceed that of any previous telescope...by orders of magnitude." – Neil deGrasse Tyson (48:12)
  • Early discoveries (e.g., thousands of new asteroids) showcase how repeated imaging creates a "movie" of the sky, revealing otherwise hidden or transient phenomena.

• Infrastructure & The “Shoe”:

  • Rubin’s distinctive dome has an attached building, nicknamed “the shoe,” housing powerful onsite processing hardware—a necessity due to staggering data rates.
    • "There's a lot of on-site processing because you can't just ship it all." – Matt O’Dowd (38:12)

4. The Rise of AI and Machine Learning in Astronomy (29:19 – 44:10)

• Why AI?

  • The complexity and sheer scale of data (e.g., thousands of lensed quasars) demand tools beyond human pattern recognition.
    • "Normal eye, normal intelligence [is] not good enough to handle this problem." – Neil deGrasse Tyson (29:48)

• Pattern Recognition & Limits:

  • AI excels at spotting complex correlations humans might miss—but is also subject to training biases and "pareidolia" (seeing patterns where none exist).
    • "They're particularly good at that. But ... we look for the patterns that we think are going to be there...with [AI], you can throw in the data and it will find patterns, even if they're patterns that weren't expected." – Matt O’Dowd (32:11, 32:27)

• Training, Physics, & Serendipity:

  • Discussion on using both supervised (with templates) and unsupervised (without templates) learning, and the challenge of ensuring AI can discover truly novel phenomena.
  • Importance of balancing teaching AI with known physics, but also leaving space for genuine discovery.
    • "If you are training it for what you expect, it's not gonna find something that nobody expects. It's serendipity on the frontier of science that we all cherish so highly." – Neil deGrasse Tyson (33:49)

• Impact on Training & Careers:

  • Debate on whether automation and AI actually “frees up” scientists for more creative tasks, or risks losing “muscle memory” intuition.
    • "The hope is...graduate students can be freed up to do better things. That is the hope." – Matt O’Dowd (39:58)
    • "You have a whole other relationship with it when you classify galaxies by hand… It's almost a muscle memory." – Neil deGrasse Tyson (42:23)

5. Science Communication, Inclusivity & Wonder (52:40 – End)

• PBS Space Time’s Approach:

  • Matt emphasizes there’s a huge appetite for seeing “under the hood” of science, not being “babied,” and for genuine deep dives. His approach is to clarify jargon and communicate real science without talking down.
    • "There's a huge appetite for seeing under the hood, like how science happens, how scientists actually talk." – Matt O’Dowd (53:36)

• Science as Collective Human Achievement:

  • Both Matt and Neil highlight a belief in science as something humans do together, not the exclusive domain of a few, and stress the accessibility and excitement inherent to scientific thinking.
    • "We should be proud of humanity for figuring this stuff out. We figured this out." – Matt O’Dowd (56:22)

• Science, Sports, and Societal Value:

  • Compare science and sports as arenas of human achievement. There’s a call to break down the barriers making science seem less accessible than other pursuits.
    • "Science is not so specialized...that you can't do it. You can actually do it if you want." – Chuck Nice (57:18)

• Importance of Scientific Literacy:

  • Chuck closes with the vital point that public engagement in science is necessary—not just for scientific progress, but for democracy, society, and the future.
    • "It's imperative for democracy, for our species...the world...with respect to the things that information that we know that will save us." – Chuck Nice (61:44)

• Teaching & Inspiring the Next Generation:

  • Neil reflects on the evolution from teaching in classrooms to public communication, emphasizing the value of sparking curiosity first—deep detail can follow.
    • "There's no obligation in a person's first encounter with your expertise for them to learn everything on your syllabus. Pick the stuff that's really cool. Teach them that. Then they say, this is cool. I want to learn more." – Neil deGrasse Tyson (62:53)

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Notable Quotes & Memorable Moments

• On Quasars:
  "The quasar is something very different to that [a star], but to 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 blare radio emissions. So they have these jets, all this cool stuff." – Matt O’Dowd (05:52)

• On the Power of Gravity:
  "The energy is in the falling...this gas falls a long, long, long way." – Matt O’Dowd (09:09)

• Tyson’s Analogy, Science as Food:
  "All I was was food for them...There wasn’t the fame; it was the fact that I had excited their curiosity." – Neil deGrasse Tyson (59:25)

• On Using AI:
  "Our big but 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 and machine learning." – Matt O’Dowd (31:28)

• AGI – Artificial Graduate Student Intelligence:
  "You know, an AGI – Artificial Graduate Student Intelligence – can now do a thesis in an afternoon." – Matt O’Dowd (40:21)

• Science Communication:
  "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." – Matt O’Dowd (54:02)

• Science as Collective Human Achievement:
  "We should be proud of humanity…for figuring this stuff out. We figured this out." – Matt O’Dowd (56:22)

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Timestamps for Key Segments

• Quasar Basics and History: 05:17 – 13:20
• Gravitational Lensing Deep Dive: 20:24 – 26:16
• Big Data & Rubin Observatory: 29:17 – 38:10 & 48:00 – 52:28
• AI in Astronomy: 29:19 – 44:10
• Science Communication & Philosophy: 52:40 – End

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Episode Takeaways

• The next generation of telescopes and sky surveys (e.g., Vera Rubin Observatory) will generate astronomical (literally!) volumes of data, requiring AI to analyze and interpret the cosmos.
• Quasars, once mysterious, are key markers of galaxy evolution powered by supermassive black holes.
• Gravitational lensing lets us peek deeper into the universe and measure cosmic events with astonishing fidelity.
• AI promises new discovery potential but also challenges: we must ensure our algorithms are open to serendipity, not just the expected.
• Science belongs to all humanity, and true engagement means inviting anyone to share in the “wonder”—not just the answers, but how we find them.

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Final Thought:
"It's so important ... science is just thinking carefully ... a state of curiosity." – Matt O’Dowd (62:47)

Until next time... Keep Looking Up!

Transcript

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[C]

Chuck I love having Matt o' Dowd back on. Definitely catches up on quasars, on the Vera Rubin telescope, on big data and AI.

[B]

Yeah, and I found out that Vera Rubin is actually not a sandwich.

[C]

Coming up on StarTalk. Welcome to StarTalk, your place in the universe where science and pop culture collide. StarTalk begins right now. This is StarTalk. Neil DeGrasse Tyson, your personal astrophysicist, got with me. Chucky. Nice.

[B]

Hey, Chuckie baby, what's happening?

[C]

How you doing?

[B]

Doing well.

[C]

You know what edition of StarTalk this is?

[B]

Which one would that be?

[C]

The Matt O' Dowd edition.

[B]

Oh, that's always good.

[C]

Matt O' welcome back to StarTalk.

[D]

Such a pleasure to be here. It's my favorite subject.

[C]

Oh, 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

[D]

my high school, the Bronx High School of Science. Don't I know it.

[B]

Oh, wow.

[C]

It was. Yeah. Oh, yeah.

[B]

Is that a feeder school to, I

[D]

think the Bronx High School of Science Go to. Straight to the Ivy League.

[B]

They go straight to the Ivy League, huh?

[D]

Yeah.

[B]

Okay. Oh, well.

[D]

But some of them do.

[B]

That's CUNY's loss.

[D]

Some of them come and hang out. Yeah, it's a good.

[C]

We can hang out there. We've been known to hang out.

[B]

Okay.

[C]

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. And I just so appreciate the work you put into what's on it, how you deliver it. And you're just so casually smart.

[D]

Casually. But I work very hard.

[C]

I know what I'm saying. The effort. I see and I feel the effort all behind you just being casually smart in those videos.

[B]

It's like those $800 rock star haircuts to make it look like.

[D]

It look like you just got rolled out of bed. Yeah, but you spent 12 hours at the salon.

[B]

Exactly.

[D]

That is the perfect metaphor for PBS space time.

[C]

So you have a research specialty in quasars? If memory serv. Is that correct?

[D]

Quasars are a star. No pun of my research. There are one or two things involved, but yes, the quasar is the coolest thing in space.

[C]

I cannot argue with that. And also lately you've been into AI and machine learning.

[D]

AI and machine learning. Exactly. I found that regular eye was insufficient for my needs.

[B]

Regular eye.

[D]

Turning to the artificial.

[B]

I got regular eye. That's where I am. You have sub regular eyes, sir.

[C]

And about the Vera Rubin telescope, which had first light last year, I think, didn't it?

[D]

First light? You know, they kept pushing it. It was actually just a few months ago, the official first light. So it was.

[B]

Okay.

[D]

It was a little more recent, so it's in the calibration mode at the moment.

[C]

Okay. Soul engineering mode.

[D]

The survey has not yet started.

[C]

Okay. The official data taking survey. Excellent. Okay, well, so welcome back. Now tell me, quasars. Remind us what that is an acronym of.

[B]

Wow.

[D]

Okay, so. And there's a whole history. No, I'm just gonna say it's quasi stellar radio source.

[B]

Okay.

[D]

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 a telescope.

[C]

Observationally, it's like a star.

[D]

Exactly.

[C]

Stars are not pinpricks of anything.

[D]

Indeed. If, 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 mirror earthlings looking out, we see these pinpricks of light. With telescopes, you can't see them with the naked eye. Some of them also blare 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.

[C]

What's the latest on quasar? 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 gotta be some other way. And they're all far away. How come there's none nearby? So catch us up on quasars.

[D]

All right, let me catch you up on quasars. So, you know, there were the pin bricks, 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 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.

[C]

But the black hole, anytime we're on the frontier, we don't know what's going on.

[B]

Right.

[C]

That opens the floodgates for theorists, of course.

[B]

Yeah, yeah, yeah.

[D]

So many papers. But 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.

[C]

Yeah, but if black holes suck, how are you getting energies out of it?

[D]

That's true, that's true. Stuff gets in, but it doesn't all get in. I mean. So 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, I mean, 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, et cetera, then you end up with this screaming vortex of material pouring into the.

[C]

When you say driving, you mean a mechanism to move stars, gas, and other material from wherever its orbit is down into the sun.

[D]

It's in the galaxy. Yeah.

[C]

It has to drop in somehow.

[D]

Yeah. Like galaxy collisions or like, you know, close. You know, there are ways to do

[C]

it because it wouldn't otherwise have an excuse to be there. You have to find a way. Somebody's got to take it out of its orbit.

[D]

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.

[C]

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?

[D]

Yeah, I mean, it's the energy of falling. And this gas falls a long, long, long way.

[B]

Can you elaborate on that? The energy falls.

[C]

We did a whole thing on this. You get in an elevator, right?

[D]

Yeah.

[C]

And it takes you to a top floor.

[B]

And then you store up all the energy as potential. And then when you drop, that's energy release. That's the energy release. Is that what he's talking about?

[C]

Yes.

[B]

Oh, so it works.

[D]

Hydroelectric dams work by the energy of falling.

[B]

Okay.

[D]

The water falls. Or turbines.

[B]

I get it. Because the black hole has gravity. So they're. There you go. I got it. Now, nevermind.

[D]

So the falling, the gas ends up moving at really insane speeds. Forms a whirlpool. Cause 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 infalling gas is just pure energy in the form of light photons.

[B]

Wow.

[D]

And so they shine out some of the gas gets in.

[C]

But just, just to bring closure to this elevator with you on the rooftop, right. 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? 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.

[D]

So the simplest answer is it's hot, it's searing, so it's thermal energy. In the end, you've got this whirlpool, these, the gas rubbing against itself and it reaches these insane temperatures. So that right in the middle, you know, your heater is infrared hot. The sun is visible light hot at the center. This stuff is X ray hot. It's just, it's, the temperatures are insane, but it's also violent. I mean, it's a vortex of crazy gas pulling into black holes. So you've got these fits and bursts and, you know, energy blasting outwards.

[C]

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. It must be a black hole. And the gas got so hot, it's now glowing in X rays.

[A]

Yeah.

[D]

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.

[C]

Sounds very cannibalistic.

[D]

Cannibalistic, yeah.

[C]

So that's it. So we have agreement on this model. Correct?

[D]

I mean, the evidence is in, I think, you know, we've, we've now built telescopes that are good enough that we can, you know, for more nearby ones. We can see the gas that 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.

[C]

Yeah. 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.

[D]

Its black hole is in its feeding phase. So the Milky Way black hole has been in that phase before.

[C]

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? Cause that's the light only just now reaching them. And they're seeing our supermassive black hole dining upon gases. And could we be a quasar to them?

[D]

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.

[C]

Okay, got it.

[D]

A quasar like object, for sure.

[C]

Okay.

[B]

And why would it be so different? Because of the size.

[D]

The size, man. Yes, the size. The Milky Way's black hole, at only 4 million suns in mass, is piddling for a supermassive black hole. But quasars have more like a billion. So they're. Okay, so they're the big bus.

[B]

Damn.

[C]

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.

[D]

Exactly.

[C]

And how massive is that?

[D]

Is it a. Billionaires around that?

[C]

Okay, so that's a galaxy that might have been a quasar.

[D]

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.

[C]

Wait a minute. Isn't that the black hole that the telescope imaged?

[D]

Exactly.

[C]

What do we call it?

[D]

The Event Horizon.

[C]

Event Horizon Telescope.

[D]

Yeah.

[C]

Yeah, nice. That's the black hole.

[D]

So I'm talking about. Oh, we can see the velocity. No, we have a picture of a black hole now.

[C]

Yeah, nice. Yes. That was banner headlines when it came out.

[B]

Yeah.

[C]

A tremendous collaboration around the world.

[D]

Collaboration of people, but also of telescopes.

[C]

Yes.

[D]

Because there were radio telescopes literally across the planet that stitched together their data to be able to get the resolution needed to see that very cool, wild stuff.

[C]

And this is.

[D]

Do you know about the data transfer process for the Event Horizon Telescope? No planes.

[C]

Okay.

[D]

It was too much data for them to go over the net, so they had to put them in boxes on planes and send them and stitch it together.

[C]

Okay, so this is the bandwidth. The Internet fax machines were available.

[D]

Yeah, well, exactly. I mean, we're going to talk about big data. This is an example of.

[C]

Yeah, I mean, in my day I'd be at the telescope and the World Wide Web was in its infancy and it wasn't even in public yet. I mean, just we had our own channels to get to move data. And there's a point where we had to compare the data rate transfer from our telescope to our office versus FedEx.

[D]

Yeah, no, you load up a tape

[C]

and FedEx it and reload the tape on the other side.

[D]

I traveled back from Chile a couple of times with the bag full of tapes of data. You gotta 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

[B]

or 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.

[C]

Bandwidth of Timothy.

[B]

Yes.

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[C]

So tell me what role gravitational lenses have played in this. And 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. That 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.

[B]

But I think before you do that, you might want to say exactly what gravity even though you just gave. Like if somebody knows what it is. But it's. It's kind of confusing. When I first read about gravitational lensing Because I'm thinking of actual lenses and looking through like this, how you do

[C]

it, you say, okay, okay, Mr. PBS, space time, explain that. Okay, yeah.

[D]

So like, you want me to explain stuff? No, yeah, so. All right, 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. Arthur Eddington goes to see an eclipse, finds that the positions of the stars behind the.

[C]

A solar eclipse.

[D]

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 funhouse 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 you have 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.

[C]

Was that called the Einstein Cross?

[D]

The most famous one is called the Einstein Cross.

[C]

Yeah. Because it was a quadruple lens object.

[D]

Exactly. One of the early ones that was found. Exactly.

[B]

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.

[D]

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. That's a powerful point.

[C]

Wait, wait, hold on. You only know that if something happened on the quasar that you can start the clock. If it's just a static image, you would.

[D]

Exactly right, Exactly.

[B]

That's fantastic, isn't it? I mean, that's just freaking fantastic. That means you can predict.

[C]

Yes. You know, you can say, oh, this, this burped over here.

[B]

Right? Watch this.

[D]

Wait for it.

[C]

Wait for it.

[B]

That's amazing.

[C]

Wait for it.

[B]

It's almost like you have like a tiny little reset time machine that you're able to observe what's happening, you know,

[C]

and say, well, let me see that again.

[D]

They're called gravitational lenses because it's gravity kind of acting as this galaxy sized 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. 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.

[B]

Gotcha.

[C]

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?

[D]

Very much so.

[B]

It's a telescope booster.

[D]

It's a telescope booster.

[B]

Oh, nice.

[C]

Very good, Chuck.

[B]

Yeah.

[D]

Yeah.

[B]

Cool, man.

[D]

It's not. It's not.

[C]

Or enhancer, I would say.

[D]

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. It was ground wrong, and they had to do a lot of work to recover the images. Likewise, these crappy lenses.

[C]

When you say the word ground, you mean when you take the shape of exactly.

[D]

Of the mirror, it's the geometry needs to.

[B]

Wait. Has it happened?

[D]

Oh, God, yeah.

[C]

You don't remember that it had the wrong shape. Where were you?

[B]

Where was I? I'm gonna tell you where I was. Hubble had the wrong. I was watching Japanese anime. That's where I was.

[C]

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 optics and compensated for that other shape and then put it right back in.

[B]

Like LASIK surgery.

[C]

Okay.

[B]

Yeah.

[C]

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. Okay. Until it was too late.

[B]

Yeah, still a dumb ass mistake, but go ahead.

[D]

Works great.

[C]

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.

[D]

Why yes, that would make sense.

[C]

But now it's farther away. But now it's dimmer.

[D]

Yeah, I mean, so there's a trade off. You also want like there's a perfect configuration of distance to 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.

[C]

And so because there's less stuff, less chance of stuff being. And nearby would be how far away?

[D]

Downtown New York. No, nearest quasars.

[C]

How far out are those?

[D]

I mean the nearest ones, you know, so M51 is the nearest.

[C]

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?

[D]

Yes.

[B]

Okay.

[D]

No, let me. Sorry. 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 by statistics, you have to look a long way to see the first one. Right.

[B]

Okay.

[D]

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.

[C]

Right.

[D]

Oh, there's a galaxy there. But we see it because of the quasar.

[B]

And you can't see the galaxy cause it's not as bright as the quasar.

[D]

Exactly.

[B]

Okay.

[C]

But there are galaxies we wouldn't otherwise seen were it not for the fact that they were far away and lensed.

[D]

That's also true.

[B]

Yeah.

[C]

Yes.

[D]

I mean, we see galaxies far away nowadays.

[B]

We're getting a real assist from the universe itself.

[C]

Yes.

[D]

Yeah. No, lensing's very powerful.

[B]

That's crazy.

[C]

It's the best predicted by Einstein. Another crumb that just fell off Einstein.

[B]

He wasn't even thinking about it.

[C]

He wasn't even thinking about it.

[B]

He's like, you know what? There might be something called gravitational lensing. I know that. That's exactly. Who cares?

[C]

That is exactly how that Went down, Matt. You know, that's how that went down.

[D]

He actually didn't think we would ever observe, like, gravitational lensing out in space. He thought it would be too weak and too far away.

[C]

Oh. Actually too rare, because he was only thinking of it in an exact alignment of two objects. And then you get an Einstein ring.

[B]

Right.

[C]

Because if you're slightly off, then it splits the image.

[B]

It sports the image.

[C]

But if it's exactly on it.

[B]

Wow.

[C]

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.

[D]

But also in 1915, when general relativity was published, we didn't even know that the universe existed outside the Milky Way galaxy.

[C]

Very good point.

[D]

Maybe Einstein did.

[C]

But no, no, no.

[B]

Hubble.

[C]

Hubble. 15 years later, 29 years, Hubble came

[B]

along and was just like, yeah, there's a lot more.

[C]

Yeah.

[B]

All right.

[C]

So thanks for catching me up on. On quasars. So these sound like complicated problems that need more than just I to solve.

[D]

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 100 of these lensed quasars, then 300, but we're about to find many thousands.

[C]

This comes to you from surveys.

[D]

It's all going to change with Reuben, which will discover many thousands of these lensed quasars. Okay. Countless other things. And so

[C]

is normal eye, normal intelligence not good enough to handle this problem?

[D]

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

[C]

statistically within the quasar, stars moving around

[D]

within the quasar, stars moving around within the lensing galaxy. That changes the way the lensing works. It's very messy, but it's powerful because those stars, they, like, sweep across the inner structure of the quasar, like this kind of radar. Voom, voom. And they can map it. They can map it at the same resolution as the event horizon telescope. The thousands.

[C]

Wait, wait, so I misunderstood. You're saying the galaxies that are lensing the quasar, the movement of stars within those galaxies give you varying patterns.

[D]

Exactly.

[C]

In the quasar itself.

[D]

Exactly. Yeah.

[C]

That's what you were saying.

[D]

That's what I'm saying.

[C]

Oh, my gosh.

[D]

Yeah. You have a quasar. You don't really know what's happening in

[C]

the middle of the freak out?

[B]

Oh my gosh.

[C]

That's what you said.

[D]

Right?

[B]

Right.

[D]

Okay, but imagine this distant quasar. It's very. 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.

[B]

Wow.

[C]

But, but, okay, like P.B. herman said, everyone has a big butt. Okay, go.

[D]

So our big but 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.

[C]

So not just I. Intelligence. Artificial intelligence. Artificial intelligence, which presumes that the artificiality of it is better at it than you are.

[D]

Yeah, I mean, it's such a catch all term. And it's not, you know, we're not putting them into ChatGPT, where sophisticated neural networks of different types to do various of these.

[B]

I mean, I don't, I don't want to sound glib, but it's kind of a matter of just pattern recognition at that point. Right.

[D]

You know what? Glib, that's, that's exactly what it is.

[B]

Okay.

[D]

These things can be very good at pattern recognition better than we are. They can, they can find.

[B]

They see Jesus in a piece of toast.

[D]

If you, if you train tortillas, you

[C]

get them all the time.

[D]

The answer is absolutely. They're particularly good at that. But 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 different types of AI model, you can throw in the data and it will find the patterns, even if they're patterns that weren't expected.

[C]

But my question too is because we are excellent pattern recognizing creatures, so good at it that we will see patterns even that aren't there.

[B]

Right?

[C]

So is AI equally as susceptible?

[B]

Is it that good that it's as bad as we are? Is it so great as bad as we are?

[C]

That's a sentence I wanted to stay, but that's it.

[B]

It's so good.

[D]

So there is that. And there's also the fact that it will find what it's expecting to find, just like we will. 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.

[B]

Yes.

[D]

And you show it a cat 100% of the time, it knows it's a cat. A dog. It knows it's a dog.

[B]

Right.

[D]

If you show it a chipmunk, it will definitely say it's either a cat or a dog, Right?

[B]

Yeah.

[D]

So it'll see what it all depends on.

[C]

Dumbass.

[D]

It's how you train it.

[C]

Chipmunk.

[D]

Okay. We trained it on chipmunks. Okay. We trained it on. So these are some of the challenges. It's as good as what you put in.

[C]

Yeah. But if you are training it for what you expect, it's not gonna find something that nobody expects. It's serendipity on the frontier of science that we all cherish so highly.

[B]

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.

[C]

You have to know what pattern means.

[B]

But it already does. It can extrapolate from the patterns that you already train it on. So then what you can do is.

[C]

But then it's still extrapolating from a given pattern. Can you find a pattern for which there is no template?

[B]

Oh, wow.

[C]

That's the question.

[B]

There you go.

[C]

Okay, so can AI find a pattern on which it has not been trained?

[D]

Yes.

[C]

And that can be an authentic pattern, not something in its imagination. Like when we find patterns that aren't

[D]

there, 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.

[C]

Because it's foundational.

[D]

Yeah, 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? But there are. So we talk about supervised learning and unsupervised learning, and there are techniques for unsupervised learning where you didn't tell it anything. You just said, what do you see? What do you see? And then it'll tell you something. And then it's your job to interpret what that means and why those patterns emerge.

[B]

And has the answer ever come back a bunny rabbit?

[D]

It's usually chipmunks, actually.

[B]

Well done, sir.

[C]

So is that precisely how you're using your AI?

[D]

We do a bunch of things, and Reuben and the other surveys do a bunch of things. In our case, we use variational autoencoders 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 what we call the latent space, and then use that latent space to try to reproduce the data in that latent space. We know 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.

[C]

Right. You need to know the physics of everything going on. If you miss some physics, you don't know what you're doing.

[D]

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 answer. So, you know, there's ways around the chipmunk problem.

[C]

You're checking the sensitivity of the system,

[D]

sensitivity when you break it, and brittleness

[C]

and see how you.

[D]

Exactly right. Yeah.

[C]

Okay.

[B]

That's pretty wild, man.

[D]

I mean, by the way, I want to say these are, were all done by my graduate students and postdocs. I, I, Good for you.

[C]

Exactly.

[D]

Well, Bahamas did come into it, actually. No, let me, that's another story.

[C]

I don't need to hear your Bahamas. That's fine.

[D]

The, if anything, I found just the smartest people to do this. And, and I mean, these days I, you know, I learned programming on fortran, right?

[C]

As did I. Yeah. I confess, for me, it's quite difficult

[D]

for me to, to really grok the, the ins and outs of all of this. Right. So I, I just try.

[C]

You an old fart.

[D]

Oh, yeah.

[B]

It doesn't make a difference. You already did your time. Yeah, you did your time. You did your time wisely 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?

[D]

It's beautiful, really.

[C]

So we've seen photos of the Vera Rubin 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?

[B]

Yeah.

[D]

To me it always looked like a shoe.

[C]

It looks like a shoe sticking out. Cause it has a rounded front. I presume that's where all the data, the big data is happening.

[D]

Yeah, I think they stack the data in there. I think they, I mean there's a lot of.

[B]

And then they fly it up to.

[D]

There's a lot of on site processing because you can't just ship it all.

[C]

So is it because this telescope is uniquely in need of data processing support that it was conceived and designed this way?

[D]

Well, there's also, 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

[C]

tour of the shoe.

[D]

You have the shoe?

[C]

Yeah, 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?

[D]

It's such early days in this revolution that it's hard to say where it's gonna land right now. It's is insanely powerful in many, many respects. It takes away work that, you know, we didn't want to be doing anyway. Yes, there'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.

[C]

And I know that's what I'm getting at.

[D]

The hope is, oh, well, now graduate students can be freed up to do better things.

[C]

Yeah, more things.

[D]

That is the hope.

[B]

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.

[D]

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.

[B]

Right.

[D]

You know, an AGI, Artificial graduate student intelligence.

[C]

Very good.

[B]

Can now that was nice.

[D]

It was do that. They can be do a thesis 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 now. I don't get this stuff. Please do this now.

[B]

Do this now. Let me ask you this. Is there any benefit to the graduate students doing the grunt work does that? Is there something that can come out of that for our brains?

[C]

I'm gonna say no. Here's why.

[B]

Really?

[C]

Okay. In my day.

[B]

In your day, okay.

[C]

Pre.

[D]

AI.

[B]

Okay.

[C]

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.

[B]

Exactly.

[C]

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's all spherical trigonometry. Gone. We just push a button and it's gone. The telescope calculates it.

[B]

Now, let me ask you this. You took spherical.

[C]

I do not.

[B]

But you took it, right?

[C]

No, no, no. It was like two years before I got there. We stopped. They stopped doing it.

[B]

And do you understand spherical trigonometry? No. That's the real question.

[D]

I mean, the question is, are there intuition?

[C]

No, I. I can.

[D]

That we miss now. I think. I think there could be intuition.

[C]

There's an intuition thing. Yeah.

[B]

That's really what I'm talking about.

[D]

Like going through the wax on, wax off of graduate school. Oh, what were you learning? I don't know. But now I know kung fu. We don't know what we lose, I guess.

[C]

Interesting. 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.

[B]

Okay. Okay.

[C]

World's expert.

[B]

Okay.

[C]

There's never been an expert such as him either before or since.

[B]

Okay.

[C]

Okay. And so I'm in one of his classes. We are classifying galaxies. Okay. It's like, this is stupid. Why am I doing this?

[B]

All right.

[C]

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 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.

[B]

And to what I was saying earlier, who knows what inspiration that is inspired by just that reservoir of seemingly useless knowledge?

[C]

Like you said, the wax on wax Off. Yeah, that's precisely the.

[D]

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, and 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, you know, when it kind of helps. Well, that, you know, it helps you know, what the true capabilities are. You know, how you're smart, what the vulnerabilities are.

[C]

No, you don't.

[D]

Yeah, like, you don't know what the vulnerabilities are. If it's just a black box.

[C]

I guess if it's a perfect black box, there are no vulnerabilities.

[D]

Okay. So we just need to build that.

[A]

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[C]

Let me just bring closure to this data challeng 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.

[D]

Orders of magnitude.

[C]

By orders of magnitude.

[B]

Wow.

[C]

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.

[B]

Right? Yeah.

[C]

And you could do that because it's taking its repeated imagery, which is the only way you'll know if something moves because otherwise it's a still frame, it's a stack shot. And is that a star or is it something that goes bump in the night? So that Forced us, our field, our community, to innovate in ways we didn't have to before. Right. So it was all good. Headed in the right direction there. Yeah.

[D]

Do you mean as astronomers or as a conversation? All of the above.

[B]

Oh, that's great. You were so incredulous.

[C]

You were.

[D]

You're like, yeah, I'm offended.

[B]

No, Neo, I'm wasting my life.

[D]

It's funny. Cause it's true.

[C]

I only say that because we had needs for imaging the universe that exceeded what Kodak now, many decades ago, routinely produced. And so 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. And before anyone even knew what they were.

[B]

And poor Kodak did not take advantage of that.

[C]

Kodak said, it's just a flash in the pan.

[B]

They're like, we're gonna do this for these nerds.

[C]

They love film. Yeah. These eggheads need these. They'll love film all the time. So. So only when it became a commodity 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? Or are advertisers mining data off of social media accounts? And is that a greater repository of data for them to sell us product?

[D]

I mean, it's greater for them in terms of bits. I don't know if it's greater the it. So it's enormous. It's, you know, for the first time, taking a full image of the entire Southern sky every three nights for 10 years.

[B]

Wow.

[D]

It's. I can give you some numbers about how much data that is. Like, the CCD is enormous. It takes like 400 HDTVs to show one image.

[C]

400.

[D]

400. And that's one image. And, you know, the southern sky is like 3,4000 of those images.

[C]

Just to be clear, the Hubble telescope field of view is a fraction the size of the full moon.

[B]

Wow.

[D]

And this is like 40 times the size for one of those images of the full moon.

[C]

That's amazing. So that's how you can. Because. So if you said, hubble, give me an image of the whole sky. Okay, call me in 30 years when I finally.

[B]

I'm gonna have to stitch this all together.

[C]

They're gonna Mosaic this, Right.

[D]

And so call me in three days,

[C]

two days for the whole sky.

[B]

Wow. That's the Vera Rubin.

[D]

Okay. 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 mainlined for 10 years. And what do we do with it? Well, we work very hard.

[B]

You guys are making like a flip book of the universe.

[D]

It's a flip book of the universe. It really is.

[B]

Holy crap. But it's a big fan flip book.

[D]

It's hard to the pages. It's like several thumb across the flip.

[C]

You put the thumb across the page edges, right?

[B]

Yeah, that's funny. I got a paper cut. Yeah, I lost my hand.

[C]

So just to anchor this in proper context, at the risk of repeating myself in my day, you know, you go to the telescope, you take a picture, and you take the picture home and analyze it. Right. If something moved, you would 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.

[D]

Well, there definitely are. We know there are.

[B]

No, no, no. Okay.

[D]

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.

[C]

Yeah, yeah, yeah.

[D]

It's tough.

[C]

Does that pump? So 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. And it's a mix of not only what is frontier science, but what also is fun science. So it's clear that you're doing some cherry picking of what you could be talking about.

[B]

Plus, you look like the sexy professor that one might daydream about.

[D]

Why, thank you. The other thing we go.

[C]

So share with me some of your tools and tactics that you found most potent in your efforts to bring the universe down to Earth.

[D]

You know, so PBS space time fills a particular niche, I think, which is that we do go hard. Like we, you know, we've covered the holographic universe and quantum mechanics.

[C]

You've been out there. Yeah.

[D]

All the way to the edge of the holographic universe. And you know, as well as, you know, 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, I think for the longest time, people have felt a Little bit babied by, by a lot of popular science media.

[C]

And they know when they're being talked down to.

[D]

They know when they're being 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 you know, 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 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 professionally.

[B]

We're not trained for that. It's all shop talk for science talk. It's all shop talk.

[C]

Yeah, but all professions have shop talk.

[B]

Exactly right.

[C]

So what's different about science?

[D]

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.

[C]

And every time you get a subfield of a subfield of a subfield, it's

[D]

excluding of others because of the language and because it's, you know, it attracts a certain type of people, nerds who

[B]

are really interested and they enjoy going granular on information.

[D]

Yeah. So I think over time science has become, I kind of think of it as genrefied. So, you know, something is sciency if it's hyper focused and detail oriented, when really it's about curiosity about the world.

[B]

Right.

[D]

So I think the beginning of science and what it really is is just a, it's curiosity with organization. And this is where we are now after centuries and millennia of doing this. We know a lot. Right. 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. You know, I rarely say inclusive. Inclusive and collective 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.

[C]

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. Not based on.

[B]

Not based on Uncle Fleet, because somebody before you. Right, right. But also. So it's lazy when people do that.

[C]

It's completely lazy.

[B]

Very lazy. Because what you're saying, it's like when people say, we did it. We won the World Series.

[D]

Who's we?

[B]

And I'm like, really? What now? When's your contract up to, like, shut the hell up?

[C]

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

[B]

that, without a doubt. But the fact is that I'm going to say we.

[C]

We figured out how to build a suspension bridge and figure out how to go to the moon. And I don't want you coming behind me saying, well, what part of the project did you work on?

[B]

Well, no, there's a different story that's different, though, and here's why.

[C]

Because it's my tax money that went in.

[B]

Not even that. 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.

[D]

But I think the public sense is the opposite. I think the public sense is that sport is more accessible than science.

[B]

And that's my point. But what my point is this sport is not more accessible. Of course, you can never hit a home run in a major league park. What you think you can do. It's never going to happen.

[C]

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, but they say, that's too hard. I want to do this easy route.

[B]

I want to do this easy route. And it's the hardest thing in the world. You're never going to do it.

[D]

But the thing is, kids. Kids play basketball, and it's not. 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.

[B]

Right.

[D]

Because the kids who do science, they're the nerds.

[B]

But you guys, 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.

[D]

Yeah, 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.

[C]

Can I tell you my transition? Thank you for saying that. That was insightful.

[B]

Oh, cool. Listen, even a broken clock is right twice.

[C]

Okay? Broken clock.

[D]

You want one more? That's right.

[C]

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.

[B]

Okay.

[C]

They didn't care what my favorite color was. As my visibility 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.

[D]

When was the last time you told someone about black holes? Has it been a while? I mean, everyone you tell about black holes.

[C]

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 and I said, neil, do you realize you're a scientist and people want your autograph?

[B]

Right?

[C]

You're a scientist and people want to take a selfie with you like a rock star or athlete. And so I had to real because they're not gonna go to the. They're not gonna go to the athlete and say, please explain your.

[B]

They just want the thing they wanna be. They wanna connect with the actor.

[C]

They just wanna connect in that one way. And so I realized, oh my gosh, the science is sharing the space that had only previously been occupied by.

[B]

And it's happening more and more. By the way, I gotta. Alex, our producer, you were not here. I've never said this to you, but I'll say it now. Cause 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 off science? Am I lying, alex?

[C]

Yeah. Yeah. There might be just a few dozen scientists who became.

[B]

It's What I'm saying is. 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 science to continue to thrive 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.

[C]

He's running for office soon. You got my vote.

[D]

But it's also. 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.

[B]

Absolutely.

[D]

That are absolutely not restricted to what we now think of as science. It's just thinking. It's just thinking carefully.

[B]

Absolutely.

[D]

And it's so important in all this.

[C]

A state of mind, a state of thought, a state of curiosity.

[B]

I love it.

[C]

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 for them to learn everything on your syllabus. Throw out the stuff that's boring or uninteresting. Yeah, it's gotta be there in a classroom. Cause you need the sequencing. But pick the stuff that's really cool. Teach them that. Then they say, this is cool. I wanna learn more. And then they're at a level where they can get the nuances that you left out in the first place.

[D]

Yeah, yeah. But also, like, throwing in a few, like, little deep views is nice. Also, it's like, oh, these are no teasers for the future.

[C]

Oh, oh, yeah, yeah, yeah, yeah. But I'm just saying, often there's detail that's just simply unnecessary.

[D]

Sure. Yeah.

[C]

At the first pass.

[D]

And it's hard to know how to spot that and excise it. We're very precious about our details, us nerds, you know?

[C]

Yeah, it is. It is. Dude, we gotta end it there.

[B]

Yes.

[C]

Oh, yeah, Matt, this was fun. Thanks, Matt.

[B]

Always good having Matt on the show.

[D]

It's nice to come down to the fifth floor and chit chat about quasars.

[C]

All right, Chuck, always good to have you, man.

[B]

Always a pleasure.

[C]

And your special is still your comedy special still airing on our YouTube channel, and it's called What?

[B]

Chuck. Nice. Just smart enough because I'm sitting between these two, so that's. That's about as good as it's going

[C]

to be for me.

[D]

I love that.

[C]

I think you're smarter than just smart enough, but all right. This has been another installment of Star Talk, the Maddow Doubt edition. Yes. Until next time, keep looking up.

[B]

This episode of StarTalk is brought to you by. 6. All new McCafe drinks at McDonald's we're talking crafted sodas made with your favorite sodas and topped with velvety cold foam like a Sprite Berry Blast made from Sprite and blueberry raspberry syrup. Don't miss the dirty Dr. Pepper or orange Dream, too. And there are refreshers made from fruit flavors and add ins like Popping Boba and freeze dried Dragon Fruit. Try the BlackBerry Passion Fruit Refresher, Mango Pineapple Refresher or Strawberry Watermelon Refresher with freeze dried strawberries. Try all new McCafe drinks now at McDonald's.

[E]

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