Latest / From First Principles / Cloud9 Dark Matter Halo, Jellyfish Sleep, and String Theory Hidden in Nature (EP. 22)

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Cloud9 Dark Matter Halo, Jellyfish Sleep, and String Theory Hidden in Nature (EP. 22) · From First Principles

• 0:00
  Hello internet, this is your captain speaking Lester Nare joined as always by my co-host and our resident PhD Krishna Chowdhury.
• 0:09
  This is episode 22.
• 0:10
  We have a fantastic three-story episode set up plus the rundown today.
• 0:16
  We're going to touch on string theory, biological networks,
• 0:20
  three-dimensional geometry we have a neuroscience and sleep story and as always we can't forget astronomy with an interesting star formation topic my friend how's it going it's going well it's going well we're in january it's 2026 yes it's 80 degrees outside it's pretty nice pretty nice there was no snow in big bear oh yeah this weekend that was a little bit unfortunate
• 0:46
  Yeah, that's kind of surprising given like how much precipitation we've had in Southern California.
• 0:51
  So exactly.
• 0:52
  I was like, oh, it's going to be snow.
• 0:53
  It was all in the desert.
• 0:55
  Yeah.
• 0:56
  A couple of housekeeping notes for this episode.
• 0:58
  If you didn't catch it on social, our new website, ffppod.com is now available.
• 1:05
  It's great.
• 1:06
  You can get all of our past episodes.
• 1:08
  You can also see and search through all of the research papers that we cover in each episode.
• 1:13
  Yeah.
• 1:14
  We have a whole variety of details, key takeaways, links out to the original paper.
• 1:19
  And we also have introduced our new submissions board.
• 1:24
  So many of you have commented about, hey, can you cover this paper?
• 1:27
  Can you cover that paper?
• 1:29
  So you can go to ffppod.com backslash submissions.
• 1:34
  You'll see a ranking leaderboard of the most popular papers.
• 1:37
  You can upvote the papers you want us to cover.
• 1:40
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• 1:41
  We're trying to engage the community here, so we're super excited about that.
• 1:44
  If you haven't taken a look, take a look, because we will have more coming to the site soon.
• 1:48
  I don't know, maybe a leaderboard or something?
• 1:50
  Yeah, something like that.
• 1:52
  So I think what's so fascinating...
• 1:56
  about the two-way street of like the social media era right it's like the response to our stuff is real time yeah right so you know immediately the day an episode comes out we're getting people like this is fantastic like the econophysics feedback from the last episode was fascinating yeah yeah yeah there were a lot of people who i think are probably in finance that had technical degrees that um are watching and hey welcome welcome
• 2:24
  We're going to learn something today.
• 2:26
  We have some fantastic stories.
• 2:27
  And as always, this is from First Principles.
• 2:46
  Okay, so for our first story, we are going to go into the world of string theory in a paper out in Nature published on January 6th.
• 2:58
  Paper is about nature's hidden blueprint.
• 3:01
  How Physical Networks Follow String Theory Mathematics.
• 3:06
  Scientists have discovered that biological networks from brains to blood vessels violate simple wiring rules and instead follow complex surface minimization patterns predicted by string theory.
• 3:19
  Yeah.
• 3:19
  Really good at predictions.
• 3:21
  This one's out of Rensselaer Polytechnic, Northwestern, the University of Chicago, and Northeastern.
• 3:27
  So a nice Marvel model.
• 3:28
  Yeah.
• 3:44
  and our vascular system.
• 3:45
  This is fascinating.
• 3:46
  It's really cool because it's not just the brain and vasculature.
• 3:49
  It's all over biology.
• 3:50
  If you look at mycelia and fungal networks, this has something to say about that.
• 3:56
  If you look at how trees branch out into patterns, this has something to say about that.
• 4:02
  And we've been trying to figure out the mathematics behind how these physical networks branch out
• 4:09
  And the unlikely savior came in the form of string theory.
• 4:14
  And it's really wild because it's all over the news because, you know, string theory has kind of a bad rep given that it doesn't predict anything, at least in terms of the fundamental physics that it promised to predict anything.
• 4:28
  But it's kind of cool that this mathematical physics approach is now being used for biological problems.
• 4:34
  And one of the reasons why I really loved this story is, you know, I'm a biophysicist by training, and there's so many examples of the mathematics of physics being used for something totally different being applied to biology.
• 4:53
  You know, if you look at
• 4:54
  hot field networks and associative memory in the brain, that uses the mathematics of Ising models that was being developed to describe magnetization.
• 5:04
  Or if you look at the renormalization group, which is being used for phase transitions or deep quantum field theory, that's being used to describe the starling flocks and how they do murmurations in the sky.
• 5:17
  So...
• 5:19
  It's one of these really cool examples of how mathematical physics research for something totally different can be applied to biology.
• 5:27
  And we're just discovering how life is so clever, you know?
• 5:32
  And this sort of translational nature of the frameworks of physics being applied in other spaces.
• 5:39
  Yeah, yeah, yeah.
• 5:40
  And I think that's just really cool.
• 5:43
  There's a...
• 5:44
  There's a neat line between the mathematics when we go really deep down.
• 5:50
  You know, I think that's really cool.
• 5:52
  So the core subject has to do with networks.
• 5:55
  Okay.
• 5:56
  Networks are basically things.
• 5:58
  And then those things have relationships.
• 6:00
  And usually when we talk about networks, what we use is graph theory.
• 6:04
  Okay.
• 6:05
  Okay.
• 6:06
  One of the famous networks that we all use every day is the internet, right?
• 6:11
  There's links from one page to another page.
• 6:13
  That's the relationship.
• 6:14
  And the things, the nodes of these networks are web pages.
• 6:18
  And the very famous PageRank algorithm that was developed by Larry Page out of a Stanford PhD thesis that became the bedrock of Google, a trillion-dollar company now, is
• 6:30
  realized that what they could do is use the mathematics of graph theory to understand relationships on the internet and then rank pages based on their connectivity rather than, you know, some naive approach that...
• 6:45
  If you remember Ask Jeeves, you know, we're old enough to remember Ask Jeeves.
• 6:49
  We are old enough.
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  Or like the Yahoo search.
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  They weren't as good because they weren't realizing this underlying graph structure.
• 6:56
  Google was good because it was ranking pages...
• 7:00
  With the attitude that the graph structure of the internet is what's important.
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  And this ended up being confirmed to be true not only by the fact that arguably the best business model ever invented in humanity with the best margins is Google.
• 7:16
  And that same concept then ultimately got applied to Google.
• 7:20
  Social graphs.
• 7:21
  Yeah.
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  In the rise of social media, Zuckerberg being the first to apply the same concept.
• 7:26
  Yeah.
• 7:27
  But in the context of social networking, which then became social media.
• 7:31
  Yes, exactly.
• 7:32
  And all of these things, at the end of the day, they're graphs.
• 7:34
  They're vertices.
• 7:35
  In the case of social networks, there would be individual profiles.
• 7:38
  And then a connection would be like a friendship.
• 7:40
  between the two right now when it comes to physical networks physical networks are embedded in 3d space i'm talking about the brain which has neurons connecting from one to another but this isn't isn't some abstract link there's a little physical connection of a synapse between a neuron a connecting to a neuron b right if you look at vasculature that's blood vessels that are physically connecting and all of these physical networks are
• 8:07
  are constrained by 3D geometry, right?
• 8:09
  They're constrained by thickness.
• 8:10
  How much room do I have to move in the brain, in the body?
• 8:14
  And it's not just these like 1D abstract links.
• 8:17
  So Ramoni Cajal in 1899, he's one of the pioneers of neuroscience.
• 8:21
  He's the first guy to establish the neuron doctrine, which is the idea that the nervous system is made out of tiny individual cells called neurons that connect to each other.
• 8:31
  And it's not just one giant network, right?
• 8:34
  And he proposed a neuron morphology that's driven by minimizing wire volume.
• 8:40
  These are some great drawings that he drew.
• 8:43
  He was an artist, and he could literally trace out using these incredible techniques where he would stain individual neurons, and he could trace out how these neurons connected to one another.
• 8:53
  And you could recreate things like the hippocampus, things like the optic nerve.
• 8:58
  That's incredible.
• 8:59
  Yeah.
• 9:00
  A lot of what he found even back then is true today.
• 9:02
  Like in the hippocampus, for example, he figured out that there's this cell layer called CA1, and it gets input from the dentate, which is another part of the hippocampus, and another part of the hippocampus called CA3, and so on and so forth.
• 9:15
  He was an incredible scientist, and he proposed that the way that these biological networks figure out where to go and what connections to make—
• 9:26
  is by minimizing wire volume, which basically turns into what's the minimum distance between two points.
• 9:33
  Like if I want to connect this point and this point, I just want to figure out what the minimum distance is.
• 9:38
  So you're saying the shortest distance between two points is a straight line?
• 9:41
  Yes.
• 9:42
  And that's exactly what he figured out that biology would at some point figure out.
• 9:47
  Like evolution would optimize for this, right?
• 9:49
  Yeah.
• 9:50
  He wasn't the only one.
• 9:51
  In 1926, there was Murray, who established Murray's Law, which governs vascular branching in the blood.
• 9:58
  And he figured out that, you know, you can minimize work that the body has to do by doing this minimum distance kind of thing, right?
• 10:08
  Where using that heuristic, what you can do is say...
• 10:13
  With this network architecture, the blood can maintain the blood volume, but at the same time, it does the least amount of work to get over the friction of pushing fluid through my blood vasculature.
• 10:24
  It's sort of like a biological optimization.
• 10:26
  Yes, exactly.
• 10:27
  And he's like, the simplest optimization of finding the shortest link between two nodes, that actually works.
• 10:34
  Okay?
• 10:35
  So it was an old paradigm, and...
• 10:39
  In graph theory, this is effectively what's called the Steiner tree problem.
• 10:43
  Okay?
• 10:44
  If you've got a graph of points in Euclidean space, these are the blue points in this photo, then if I want to connect them,
• 10:52
  with edges such that the sum of all the edges is smallest so it's the it's the most optimized graph that minimizes the distance between all these edges then what i can do is introduce little points called steiner points in between the vertices that i want to attach
• 11:13
  Okay.
• 11:14
  And there's a certain rule that all of these Steiner points follow.
• 11:18
  Okay.
• 11:19
  If I basically, if I want to have an algorithm that makes a graph such that all of the edges are least distance.
• 11:28
  Okay.
• 11:28
  Then there's two things that we notice.
• 11:30
  Okay.
• 11:30
  First is that all branching points have only three nodes.
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  They're called bifurcations, meaning one path is going to lead out to two.
• 11:38
  There's never one path leading out to three.
• 11:41
  Because one path leading out to three, you could actually decompose that into one path leading out to two, and then this branch leads out to two.
• 11:48
  It's suboptimal to have three coming out of a single node.
• 11:52
  Yeah, a fork is always two.
• 11:55
  A fork in the road is always two.
• 11:56
  There's never three paths out of an input.
• 11:59
  Okay.
• 12:00
  So that's the first thing that's just a mathematical truth if you want to solve the Steiner tree problem.
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  Okay.
• 12:06
  Okay.
• 12:07
  The second point is all of these intersections happen at 120 degrees.
• 12:14
  Okay.
• 12:14
  Oh.
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  And that kind of makes sense because if you've got three paths coming out, a circle is 360 degrees.
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  360 divided by three is 120.
• 12:21
  Right.
• 12:22
  I got you.
• 12:23
  So that it all like kind of makes sense.
• 12:25
  Okay.
• 12:25
  Okay.
• 12:26
  The problem is, if you look at empirical data from biology, this is not true.
• 12:32
  Okay?
• 12:32
  Okay.
• 12:32
  In recent times, we've done incredible work mapping out the 3D morphology of biological networks.
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  This right here is a neuron from the Human Connectome Project.
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  They've mapped out a human neuron.
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  And what you can see is violations of that Steiner tree.
• 12:48
  Yes.
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  You've got trifurcation, so a single path.
• 12:51
  leading out to three.
• 12:53
  And you've also got these orthogonal connections where the angle is not 120, but 90 degrees.
• 12:59
  So it is structurally, it appears to be suboptimal from an efficiency perspective.
• 13:04
  Yes.
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  And if you were to actually calculate the minimum Steiner tree and then add up the lengths of all of these guys, the total length is about 25% more than what the minimum would be.
• 13:18
  So it's requiring more surface area.
• 13:21
  to have the output equivalent to the optimal version.
• 13:25
  Yeah, yeah.
• 13:26
  Well, you're jumping the gun here a little bit because it's requiring more length.
• 13:31
  Okay.
• 13:31
  Right?
• 13:32
  Like the length is what is not optimal.
• 13:36
  I understand.
• 13:37
  You see what I'm saying?
• 13:38
  We're separating different degrees of measurement because they have different implications.
• 13:42
  Exactly.
• 13:43
  And the older paradigm was that I want to minimize length.
• 13:46
  But you got there by saying, well, what if I want to minimize length?
• 13:51
  surface area instead.
• 13:52
  Now let's think about it.
• 13:53
  Why would I want to minimize surface area?
• 13:56
  If I'm a living being, why would I want to minimize surface area rather than length?
• 14:01
  Well, the reason is surface area is the actual thing that requires material.
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  You know, I need a cell membrane and that cell membrane is the thing that I want the least of.
• 14:12
  Doesn't really matter what the length is.
• 14:14
  Right.
• 14:14
  Right.
• 14:15
  Because if I can if I can minimize surface area but have a longer length, maybe that's fine because I'm using less material.
• 14:23
  OK. And that is the insight of this new paper.
• 14:26
  Interesting.
• 14:26
  OK.
• 14:27
  The new papers introduces the fact that maybe if we minimize surface area.
• 14:33
  Then we can replicate biological networks.
• 14:35
  And what's really cool is they introduce an exact mathematical mapping between this local network design of minimizing surface area and the high dimensional Feynman diagrams that we see in string theory.
• 14:48
  Okay?
• 14:49
  Yes.
• 14:49
  That's the key insight.
• 14:50
  Okay.
• 14:50
  The key insight is string theorists have been worried about this problem for like 30 years.
• 14:56
  And if we can just borrow all their mathematics.
• 14:58
  And apply it.
• 14:59
  Here.
• 15:00
  Instead of in an abstract space, in a measurable space.
• 15:04
  Then all of a sudden we've got a heuristic, a mathematical heuristic that solves for why biology is doing this.
• 15:11
  It's a language to interpret the observations of biology where we don't necessarily have a coherent, cohesive language to describe it mathematically.
• 15:20
  Yeah.
• 15:21
  Currently.
• 15:21
  Yeah, currently.
• 15:22
  But now maybe we do.
• 15:23
  Okay.
• 15:23
  Right?
• 15:24
  That's the idea.
• 15:25
  Yes.
• 15:25
  So let's think about this.
• 15:27
  a little bit more in detail.
• 15:29
  Okay, biological networks are 3D manifolds, right?
• 15:32
  There are these 2D structures embedded in 3D.
• 15:35
  So you've got tubes bounded by membranes, bounded by endothelium, things like that.
• 15:40
  And what we want to do, the optimization is to find the minimal surface.
• 15:44
  This is what's called a plateau problem.
• 15:46
  And physical structures do this all the time.
• 15:48
  Here, you see a bubble in between two rings.
• 15:51
  that bubble naturally is going to minimize the surface area of the bubble because of surface tension.
• 15:58
  So we sort of see this concave-esque... Because a bubble, when you say bubble, it's...
• 16:05
  It's a spherical thing.
• 16:06
  But imagine now two rings, like the bubble generators, right?
• 16:10
  And they're right next to each other.
• 16:11
  You get this sort of concave surface.
• 16:13
  Yeah.
• 16:14
  Right?
• 16:14
  Because it's trying to minimize.
• 16:15
  Yeah.
• 16:16
  The surface area.
• 16:17
  This is a natural example.
• 16:19
  Yeah.
• 16:19
  Yeah.
• 16:19
  Yeah.
• 16:20
  I mean, the reason why a bubble is spherical is because a sphere minimizes surface area.
• 16:24
  Right?
• 16:24
  So the same mathematics maybe we can use for this.
• 16:27
  Right?
• 16:28
  And the other constraint is that the surface curvature has to be continuous.
• 16:31
  Right?
• 16:32
  So you can't have like kinks.
• 16:33
  You can't have like singularities, like little points, like, you know, you want a smooth surface everywhere.
• 16:39
  You want a nice, smooth, bald head.
• 16:41
  Yeah, yeah, yeah, yeah.
• 16:43
  And so with that in mind, let's now do a little bit of string theory, a deep dive into string theory.
• 16:50
  Okay.
• 16:50
  Okay.
• 16:51
  The infamous physics theory that is supposed to solve everything.
• 16:56
  Okay.
• 16:56
  Yes.
• 16:56
  Okay.
• 16:57
  And just briefly before we dive in, is this the idea that it's meant to be a theory of everything or unifying classical and quantum?
• 17:08
  Or no, that's like a different concept.
• 17:10
  No, no, no, no.
• 17:11
  This is the same theory.
• 17:12
  And it's unifying general relativity and quantum.
• 17:15
  I'm sorry.
• 17:15
  Okay.
• 17:16
  Yeah.
• 17:16
  Yeah.
• 17:16
  Yeah, but it is – that is exactly what we're talking about.
• 17:19
  This is the thing of Ed Witten fame and who's that guy who goes on Joe Rogan and talks trash all the time?
• 17:27
  I know.
• 17:27
  We're talking.
• 17:28
  Weinstein.
• 17:29
  Yeah.
• 17:29
  Eric Weinstein, I believe his name is.
• 17:31
  It's the geometric unity.
• 17:32
  Geometric unity.
• 17:33
  So string theory is purporting to do the following, which is unify quantum mechanics with general relativity, right?
• 17:39
  Copy.
• 17:40
  And –
• 17:40
  In quantum mechanics, the best picture, my favorite picture is Feynman's picture, which has to do with the particle picture, but the field picture of quantum mechanics.
• 17:49
  The way we understand the interaction between particles in quantum mechanics is that they exchange virtual particles.
• 17:57
  So here what we see is an electron.
• 17:59
  that's being deflected by an electron.
• 18:01
  You know, electrons repel each other.
• 18:03
  That's the classical picture, right?
• 18:05
  Electrons have negative charge, so they don't like to be next to each other.
• 18:08
  In the quantum picture, the reason why they don't want to be next to each other and the reason why they get deflected in opposite directions is because they're exchanging a virtual photon.
• 18:18
  Okay, and that's what you're seeing in that Feynman diagram.
• 18:20
  An electron is coming from the left, an electron is coming from the right, and they're exchanging a virtual photon, and that's how the momentum gets transferred from one to another.
• 18:29
  Copy.
• 18:29
  Okay, so that's the Feynman picture, right?
• 18:32
  In string theory, there's no longer anything called particles.
• 18:35
  Instead, all of our fundamental particles, all of our fundamental fields are made out of one-dimensional strings.
• 18:41
  So you can imagine a rubber band that's one-dimensional.
• 18:43
  It's a line.
• 18:44
  And that line oscillates in multiple dimensions.
• 18:49
  Just like a guitar string can oscillate this way or into the guitar and out of the guitar, so that's two dimensions that it's oscillating.
• 18:55
  A string can oscillate in multiple dimensions, sometimes 10, sometimes 12, depending on what theory is your favorite.
• 19:02
  And the idea here is that in this diagram we see starting from matter all the way at the top as you break down to smaller scales, molecule, atom, neutron, strings are smaller.
• 19:14
  Than that.
• 19:15
  are the most, in this theory, are the most fundamental atomic unit of physical reality.
• 19:22
  Exactly.
• 19:22
  And they're the same.
• 19:23
  The beauty of string theory is that it's all just one string, but the way that it oscillates in all these different dimensions make it an electron or a photon or a...
• 19:33
  up quark or a down quark things like that so it's this nice like unified principle right that actually there's no such thing as all these different fields and all these different particles there's only one fundamental thing and that is the one-dimensional string and the way that it moves around in these 10 dimensions gives it the properties of an electron or up quark or a down quark and all these things so it's it's a nice like mathematical unified picture that's interesting um it just hasn't found any like
• 20:00
  real-world physics use cases, because in order to prove whether these strings exist, you need to go down to the fundamental scale, like the Planck scale, of really tiny length, really tiny time.
• 20:12
  The energy is massive, 10 to the 19 gigaelectron volts, which, you know, the LHC only has 10 to the 4 gigaelectron volts.
• 20:20
  Absolutely.
• 20:20
  So we're trying to do 10 to the 15 on top of what the LHC at CERN is doing.
• 20:26
  The Large Hadron Collider, for those who might not know.
• 20:28
  That's right.
• 20:28
  Yeah, the LHC is the Large Hadron Collider.
• 20:30
  So we need to LHC the size of the galaxy in order to probe these limits.
• 20:36
  And so the point being, this is very largely theoretical, because as of yet, there's been no way to experimentally
• 20:46
  play with these theories.
• 20:48
  Exactly.
• 20:48
  Yeah, exactly.
• 20:50
  And it's been a really cool mathematics exercise on its own.
• 20:54
  If you were to just look at the mathematics, it's amazing.
• 20:56
  It's won Fields medals.
• 20:57
  Ed Witten won the Fields medal for his work in string theory.
• 21:01
  he's never going to win the physics Nobel prize.
• 21:03
  Right.
• 21:03
  Okay.
• 21:04
  Because to win the physics Nobel prize, there has to be some experimental grounding for whatever thing you do, but the fields metal is pretty nice.
• 21:12
  So I don't think he's like too worried about it.
• 21:14
  He didn't win the Oscar, but he got the golden globe.
• 21:16
  Oh no.
• 21:17
  Yeah.
• 21:17
  Yeah.
• 21:18
  Yeah, exactly.
• 21:20
  Even though, I mean,
• 21:21
  Well, I think you're going to piss off some mathematicians in the audience.
• 21:25
  Bad analogy.
• 21:27
  Bad analogy.
• 21:28
  Do not come in my comments.
• 21:30
  Maybe like something closer is like he won finals MVP, but not league MVP.
• 21:35
  Yeah, yeah.
• 21:36
  That's maybe a better.
• 21:37
  Yeah, yeah.
• 21:38
  No one.
• 21:38
  Please stay out of my comments.
• 21:41
  Yeah, but so in string theory, you know, we had that original Feynman diagram, which was looking at the interaction between particles.
• 21:48
  So in string theories, what you can do is you can turn a Feynman diagram into something called a world sheet.
• 21:56
  Okay, so on the left, we have that same Feynman diagram.
• 21:58
  And on the right, you have strings that are merging together.
• 22:02
  And they're interacting in this world sheet, which is like how they're going through space time and all these dimensions.
• 22:08
  And then they come out.
• 22:10
  And that is the key here.
• 22:14
  The world sheet.
• 22:14
  That world sheet is the key here because it turns out that that smooth merging of those 2D world sheets, those 2D string world sheets, because when they merge, they have to obey...
• 22:27
  smoothness there can't be any kinks in this world sheet because that's unphysical and they also have to minimize the surface area in this world sheet in this in this you know space whatever they're moving in and that is topologically identical to the smooth merging of biological tubes in 3d space yes that's the key insight okay yes okay yes
• 22:52
  Yes.
• 22:52
  That is the key insight.
• 22:54
  And the equivalence is pretty stark.
• 22:56
  They lay it out in their paper.
• 22:58
  You know, on the right-hand side, they've got that Feynman diagram and the world sheet.
• 23:02
  Yes.
• 23:03
  And on the left, what they're showing is if you've got these physical networks, they're going to start obeying the same physics of those world sheets.
• 23:10
  Yeah, yeah, yeah.
• 23:10
  Right?
• 23:11
  That's the key thing.
• 23:12
  The string cross-section is now our biological tube cross-section.
• 23:16
  The time evolution for strings, like how I go from past to future, for us, it's just, oh, I'm just like moving along the tube.
• 23:24
  Yeah, yeah, yeah.
• 23:24
  Okay?
• 23:25
  So it's another spatial dimension.
• 23:26
  Mm-hmm.
• 23:27
  My 2D world sheet is my biological membrane now.
• 23:30
  And the tension, the string tension, which is something that is used in string theory to figure out how tense sort of the strings are in a loose sense, that becomes the metabolic cost of surface area.
• 23:41
  Yeah.
• 23:42
  Yes.
• 23:43
  Oh, that's so, that's really.
• 23:46
  Okay.
• 23:47
  I'm tracking because I think what's fascinating is we've basically, there's been a mathematical structure that has been very, millions and millions of dollars has been spent on.
• 23:58
  Yes.
• 23:58
  In string theory to really hone in on this mathematical framework and language to communicate a conceptual idea.
• 24:05
  Yes.
• 24:06
  That when we look at these physical manifolds and like biology, right?
• 24:10
  That math that we've spent millions of dollars on almost is a perfect, as you can see from these images, almost a perfect map onto our observed biological structures that effectively take the same shape.
• 24:22
  Exactly.
• 24:22
  And potentially respond to a similar math framework.
• 24:24
  Exactly.
• 24:25
  Exactly.
• 24:26
  That's the idea.
• 24:27
  And what they're doing is they're trying to basically do the math of string theory, which is this Nambu Goto action.
• 24:34
  It's an action that measures the area of the world sheet, which is the total cost.
• 24:40
  And the equation is insane.
• 24:41
  I think I've got a little photo of it.
• 24:44
  The equation is it's minimizing this integral over all of your possibilities.
• 24:50
  And it's making sure...
• 24:53
  This mathematics is making sure that your stuff is smooth.
• 24:56
  And it's making sure that if you minimize this giant integral, that the surface area of this whole thing is going to be minimal.
• 25:03
  It's going to be minimal.
• 25:04
  The point is, this math looks insane.
• 25:07
  Looks like man-man.
• 25:09
  We've worked on it.
• 25:10
  Yeah.
• 25:10
  Because all of these physicists have been working on string theory.
• 25:13
  Yes.
• 25:13
  So we've worked on it.
• 25:15
  So we don't need to start from scratch.
• 25:16
  We can just take all of these results and apply it here.
• 25:19
  Yes.
• 25:19
  Right.
• 25:20
  And it bakes in the smoothness.
• 25:24
  And all of these mathematical toolkits that have been developed can now just be applied directly to the biological problem.
• 25:32
  So the point being the biologists don't have to spend years sort of wading through the dark of discovery around, you know, finding the four corners of the sandbox on the mathematics.
• 25:44
  It's a Lego block that already exists.
• 25:46
  They can just be like, oh, here are these glasses.
• 25:47
  Let's look at it over here.
• 25:48
  Oh my god, I can see.
• 25:49
  Yeah, yeah, yeah.
• 25:51
  It's pretty cool.
• 25:52
  It's pretty cool.
• 25:53
  So now let's see how it actually works with biology.
• 25:56
  Yes.
• 25:56
  Okay?
• 25:57
  So let's first tackle the bifurcation versus trifurcation, which is the idea of a single path going to two, according to the old paradigm of Steiner trees.
• 26:08
  But now what we see is single paths going to three.
• 26:11
  Why would the new paradigm, now that we've borrowed this math from string theory, allow trifurcations...
• 26:18
  And make that totally chill.
• 26:19
  Yes.
• 26:20
  Okay?
• 26:20
  The reason for that is because the surface area is what matters.
• 26:26
  Right.
• 26:26
  Not the length.
• 26:27
  Not the length.
• 26:28
  Right?
• 26:28
  So the wiring economy of minimum length only matters if your width of the world sheet is really small.
• 26:39
  And basically, if the thickness of your world sheet, the string, like the radius of your string, is really small, then it becomes a line.
• 26:46
  Yes.
• 26:46
  And then you only have...
• 26:48
  Bifurcations.
• 26:49
  There's not enough complexity to need trifurcations.
• 26:52
  Right.
• 26:53
  But the thicker we make the biological network, the more we might need trifurcations.
• 27:00
  And that's what you see in this paper.
• 27:02
  That's what they figured out.
• 27:03
  As we go from thinner manifolds to thicker manifolds, at some point, the two forks in the road merge to become these...
• 27:16
  Three forks in the road.
• 27:19
  Because as you get thicker, your surface area is increasing over every length of distance.
• 27:25
  Yeah.
• 27:25
  And so subsequently, it's becoming inefficient, even if you're at the same distance.
• 27:30
  Yes, exactly.
• 27:31
  And they actually introduce an order parameter.
• 27:33
  They call it chi.
• 27:34
  And that controls whether you're going to go for two or three.
• 27:37
  Okay.
• 27:37
  Okay?
• 27:38
  If it's really thick compared to the separation distance, that's the thing that matters.
• 27:43
  Yeah.
• 27:43
  You see?
• 27:44
  Yeah, that makes sense.
• 27:44
  If it's like, if it's really far away, then I could do these Steiner tree approximations.
• 27:49
  But if my branching is really close compared to my thickness, then I'm going to need to do these higher order furcations.
• 27:58
  But that makes sense.
• 27:59
  You know what I mean?
• 28:00
  And that's what this is showing.
• 28:01
  And you can actually see the prevalence in the biological data.
• 28:05
  So they got data from the Human Connectome Project.
• 28:07
  They got data from fly neurons.
• 28:09
  They got data from tropical trees.
• 28:11
  They got data from blood vessels.
• 28:13
  They got data from coral.
• 28:15
  It's just a massive amount of data.
• 28:17
  And what you can see is 15% of the time you do have these trifurcations.
• 28:22
  And it depends on that order parameter that I was telling you about.
• 28:25
  Yes.
• 28:26
  How thin is it versus how thick is it?
• 28:27
  Yes.
• 28:28
  Like how far away is the branching versus how thick it is?
• 28:31
  Yes.
• 28:31
  And they can immediately map the biological data and show that the Steiner tree algorithm is wrong.
• 28:37
  But this new heuristic that they have using the string tree...
• 28:41
  Using the string tree...
• 28:44
  Using the string theory mathematics, that's what's closer to the biological data.
• 28:50
  The idea is if you have two nodes at some distance apart, the old Steiner model worked if the distance between those two nodes where the branching was happening was less.
• 28:59
  Really far away.
• 28:59
  Really far away.
• 29:01
  But as those nodes close in distance, the thickness of the line between those two nodes or the path and branch between those two nodes, as it gets thicker and as it gets closer, again, the optimization algorithm flips from...
• 29:21
  Optimizing for two bifurcations.
• 29:24
  So now we need to start introducing three.
• 29:26
  We need to offload more and keep – it's almost like the thicker you are, the shorter you need the distances, the more outlets you need for optimizing for efficiency.
• 29:38
  Exactly.
• 29:38
  And what's really cool to me is that across all of these different biological networks, right, we've got neurons.
• 29:45
  We've got trees.
• 29:46
  We've got fungi.
• 29:47
  We've got corals.
• 29:48
  Yes.
• 29:48
  It's all showing the same strategy.
• 29:52
  There is an under... Again, going back to what we always talk about, there is something that is true across all these different types that they all are speaking the same mathematical language that the string theorists have been trying to get everyone else to think about for so long.
• 30:06
  And it kind of makes sense because even though these guys are so evolutionarily distinct, right?
• 30:11
  You've got plants, fungi, coral, and the human brain.
• 30:16
  Even though...
• 30:18
  evolutionarily were divided by millions, hundreds of millions of years.
• 30:23
  The fundamental resource is surface area, right?
• 30:27
  The fundamental resource is how can I build this?
• 30:30
  And so I still want to optimize minimum surface area because I don't want the building cost to go up.
• 30:35
  Yes.
• 30:35
  Right?
• 30:36
  The second thing that I was telling you about the Steiner tree is all of the nodes happen at 120 degrees, but in normal biological networks, sometimes it happens at 90 degrees.
• 30:48
  Why is that happening?
• 30:49
  Well, if the thing that's coming out of my tube...
• 30:52
  is really thin, again, it's that same order parameter.
• 30:56
  If the thing that's coming out of my tube is really thin, then it's actually preferable for me to be 90 degrees away from the tube.
• 31:03
  On the other hand, if the branching is like thick, thick, thick, then I get to the 120 degrees.
• 31:10
  And that's what they're showing.
• 31:11
  They're showing that with these neurons, what you can see is...
• 31:15
  If you've got these tiny little synapses that come out of, let's say, an axon or a dendrite spine, right?
• 31:24
  Those tiny little synapses are really thin compared to the thickness of the dendrite.
• 31:30
  And so what I really want to do in that situation is come out at 90 degrees.
• 31:34
  I don't want to go at 120 because that's the thing that minimizes surface area.
• 31:39
  It's funny if you think about suburban neighborhoods—
• 31:42
  And or the design like the the design of the road networks.
• 31:47
  Yeah.
• 31:48
  You know, this looks like a cul-de-sac in a subdivision.
• 31:50
  It kind of does.
• 31:51
  You know, because you have your main two lane road.
• 31:54
  Then you have these offshoots that are narrower right off the main road at 90 degree angles.
• 32:00
  Yeah.
• 32:02
  So we've also mimicked that obviously.
• 32:04
  And for, you know.
• 32:04
  Yeah.
• 32:05
  It's the whole image of you see people with traffic going really fast in cities.
• 32:08
  And then people like map the blood vessels.
• 32:11
  Yeah.
• 32:11
  And there's the similarities in these structures.
• 32:13
  Yeah, I mean, there's structure all the way down.
• 32:16
  All the way down.
• 32:17
  And so I just thought this was really cool.
• 32:20
  In terms of the computational methodology, I thought the way that they did it was very cool.
• 32:25
  This is the Minsurf NetWa.
• 32:27
  It was implemented in Mathematica.
• 32:29
  This is the new code that they use with finite element methods to solve for surface variation problems.
• 32:36
  They actually have it available on GitHub, so you can use it.
• 32:39
  Yes.
• 32:40
  And what they really did was...
• 32:42
  They inputted the connectivity of real maps, and then they said, okay, what is...
• 32:47
  They asked the algorithm to generate the optimal surface geometry.
• 32:51
  So it makes up a bunch of meshes, of polygonal meshes, and then tries to figure out what the optimal surface geometry is, such that it minimizes surface area.
• 32:59
  And they could reproduce what the biological networks were doing.
• 33:03
  So there's sort of this generative...
• 33:05
  model that's based on mathematics yeah but it is it is not it is generating net new yeah structures and those net new structures that were generated based on this underlying mathematical framework when then held up next to our observed biological uh comparison yeah the statistics match it's just it's it's like and it's very nice it's very nice
• 33:28
  So I just thought this was a very cool paper, right?
• 33:30
  And it's kind of a unification of biological physics across so many different types of organisms.
• 33:36
  Fungi, plants, vessels, brains, coral, all of them have the same principle, right?
• 33:42
  The structure is shaped by a universal physical constraint of minimizing the amount of membrane that I need to use to create a network.
• 33:52
  Okay.
• 33:52
  Because it kind of makes sense, too, because we think, you know, the larger your surface area, the more energy you need to make it to make the things and sustain the thing.
• 34:00
  So it's like, again, if you're trying to minimize that.
• 34:02
  Yeah.
• 34:03
  Yeah.
• 34:03
  And it's not not just the energy and making the thing is the material cost.
• 34:07
  You're using more carbon atoms.
• 34:08
  Right.
• 34:08
  You're using more hydrogen atoms, which you could use for something else.
• 34:12
  Right.
• 34:12
  Right.
• 34:12
  So you want to optimize at all times.
• 34:14
  Right.
• 34:15
  There are a bunch of really cool applications that I just want to go over.
• 34:18
  One of them is 3D bioprinting.
• 34:21
  So what you can do is design scaffolds using this algorithm to create physically correct microvasculature if you want to create an artificial tissue and you want to improve blood flow in medicine.
• 34:33
  Yes.
• 34:33
  Right?
• 34:35
  So now we now basically have a pathway to 3D print things that can be integrated into our system.
• 34:40
  Exactly.
• 34:41
  That would map structurally.
• 34:43
  Yeah.
• 34:44
  more onto the way our biological system is exactly is exists to, to optimize for the potential of the body rejecting it and all these.
• 34:51
  Exactly.
• 34:52
  And, and the body not having to work harder than it should to push blood through this artificial system.
• 34:59
  Yes.
• 34:59
  Right.
• 34:59
  Yes.
• 35:00
  Another one could be hardware design.
• 35:01
  Like if you want to use the surface optimization principles to make the
• 35:06
  3D chip architectures, right?
• 35:09
  Like neuromorphic chips.
• 35:11
  You can now use these principles to do that.
• 35:13
  Yes.
• 35:14
  Right?
• 35:14
  And finally, what I want to touch on is like kind of the philosophy of science, right?
• 35:20
  There's string theory has gotten a lot of heat.
• 35:22
  Right?
• 35:23
  And it shows in this XKCD comic, String Theory Summarized, the string theorist said, I just had an awesome idea.
• 35:30
  Suppose all matter and energy is made of tiny vibrating strings.
• 35:34
  And his friend says, okay, what would that imply?
• 35:36
  And the string theorist says, I don't know.
• 35:40
  String theory has been the butt of so many jokes in physics and mathematics in the idea of funding fundamental science.
• 35:48
  And I think that's like quite dangerous.
• 35:50
  Now, there's stuff to be said about perhaps the scale at which we funded string theory versus something else.
• 35:56
  But the idea of funding something like string theory should not be...
• 36:01
  totally looked down upon just because, oh, it doesn't have any real world applications because at the end of the day, if we go into fundamental research, yes,
• 36:11
  Just being curious about how stuff works.
• 36:14
  Maybe it works this way.
• 36:16
  What would the mathematics look like?
• 36:17
  You don't know where it's going to end up.
• 36:20
  100%.
• 36:20
  And this is a perfect example of the importance of curiosity.
• 36:23
  Just curiosity-driven research.
• 36:25
  In frontier research.
• 36:26
  Again, this is the difference between research that's being done in the corporate environment and in business architectures, which are well-funded.
• 36:36
  Mm-hmm.
• 36:37
  but do not have the incentive to explore areas of research that don't immediately have a profit motive.
• 36:44
  Exactly.
• 36:45
  And there are so many things that are beneficial to mankind and all of us in our health, in our socialization, all these things that don't inherently have the profit motive, but are still valuable.
• 36:58
  Again, if you're starting to create artificial hearts and heart transplant stuff or our skin grafting and replacement therapies, yeah,
• 37:05
  based on the fact that strength there is spent all this time and money figuring out the structure.
• 37:10
  And now it's going to save people and extend your life.
• 37:13
  That's pretty cool.
• 37:13
  That's pretty good.
• 37:15
  That's pretty good.
• 37:17
  And this is, again, why we always like to talk about the methods and the concepts behind things, even if it's not an immediately applicable, like...
• 37:26
  Finding because this is what we're going to end up utilizing as we get older, as our children get older in the world around us.
• 37:34
  It's not going away.
• 37:34
  It's only accelerating.
• 37:36
  And it's a great way to start episode 22 with a little bit of balanced balance.
• 37:43
  views on string theory yeah you know we have ours not we it's still funny it's the the comic is still funny i'm gonna laugh you know uh this is this is so good and i could there's so many things about that i could dive into but we are moving on to the rundown for the week after our first deep
• 38:14
  see what else will come from that.
• 38:16
  But here's the thing.
• 38:19
  That's interesting.
• 38:20
  You have four universities involved in this story.
• 38:25
  It takes a village.
• 38:27
  Yeah.
• 38:27
  Yeah.
• 38:28
  To do these things.
• 38:28
  This is not some one person in a lab by themselves.
• 38:32
  It really requires cross-institutional and cross-functional work to do so.
• 38:38
  But that being said, we will move into the rundown.
• 38:42
  It seems like the audience likes a little quick rapid fire.
• 38:44
  So again, the concept of the rundown, we can't cover everything.
• 38:47
  All frontier science research in the level of detail we do, we would be here until the end of time.
• 38:52
  But it is still important to know briefly what's going on in the world around us.
• 38:57
  And we're going to start with our first rundown story from January 8th, The Hidden Strength of Iron Asteroids.
• 39:04
  This is a planetary defense story.
• 39:07
  The obleque of space, why iron asteroids are harder to deflect.
• 39:11
  The byline on the story is a groundbreaking simulation reveals that iron-rich asteroids possess a hidden strength that makes them more resilient to impact space.
• 39:22
  than previously theorized.
• 39:24
  This is out of the University of Oxford and CERN.
• 39:28
  And the implications here are using CERN's high-rad map facility, physicists irradiated meteorite samples with high-energy proton beams to see how they would deform under stress.
• 39:40
  They discovered that there was this dampening, meaning that the faster the asteroid is hit, the better its internal structure dissipates energy, which is a little counterproductive.
• 39:49
  Yeah.
• 39:49
  Intuitive.
• 39:50
  So this just suggests that kinetic impactors, like the idea of NASA's DART mission, which I believe was to like intercept, shoot something, intercept an asteroid on, on, on its way in.
• 40:00
  Yeah.
• 40:00
  So intercept an asteroid and change its trajectory.
• 40:03
  Yes.
• 40:03
  Yes.
• 40:04
  Yes, the idea is that it would remain intact because it would be able to dissipate the energy.
• 40:11
  The asteroid would be able to dissipate the energy on impact.
• 40:14
  Exactly.
• 40:14
  We wouldn't have like an Armageddon situation where the asteroid would break up into a bunch of chunks.
• 40:21
  And then now instead of worrying about one thing, we're worrying about nine things that are coming in.
• 40:27
  Basically, the idea is like as you hit the asteroid, if it's full of iron, it's going to like ring.
• 40:32
  Yeah.
• 40:33
  You know, and sort of dissipate its energy internally.
• 40:36
  Yes.
• 40:36
  Maybe heat up a little bit, but it's not going to totally...
• 40:40
  Break apart.
• 40:40
  Yes.
• 40:41
  Very cool.
• 40:42
  The context analogy for this one is it's similar to hitting a pool of oblec, which is cornstarch and water.
• 40:48
  If you poke it slowly, it's soft.
• 40:51
  But if you punch it hard, it turns into a solid wall.
• 40:55
  And these asteroids essentially have this hardening response to an impact, making them tougher to move.
• 41:00
  So if you're in planetary defense, we can't just bomb and shoot our problems away.
• 41:05
  Yeah.
• 41:06
  You got to get a little bit clever, but...
• 41:09
  The asteroid could do some of the work for you.
• 41:12
  Armageddon was a great movie.
• 41:13
  It was a good movie.
• 41:14
  I liked that movie.
• 41:15
  There was one of Ben Affleck's greatest works was the DVD commentary on Armageddon.
• 41:22
  I have not watched this really.
• 41:24
  Because if you remember, it was like a bunch of guys who work on an oil rig came into NASA, and they were like, we have no one else on the planet who can do this job other than a bunch of guys from the oil rig, which Ben Affleck was making fun of in his DVD commentary.
• 41:36
  He's like, why would NASA not know how to drill?
• 41:39
  Right.
• 41:39
  Oh, yeah.
• 41:40
  It's like, why couldn't we just train astronauts to do oil rigs?
• 41:45
  Maybe that's easier than training oil riggers to be astronauts.
• 41:49
  Yeah.
• 41:49
  And it was having having having a fun time taking the piss.
• 41:53
  Our story number two.
• 41:55
  Reform, an artificial metabolism for carbon.
• 41:59
  This is a synthetic biology story.
• 42:02
  January 5th, 2026.
• 42:04
  Headline, synthetic biologists transform waste CO2 into the building blocks of carbon.
• 42:11
  of life.
• 42:11
  Scientists have created an entirely new artificial metabolism that bypasses natural evolution to turn waste carbon dioxide into valuable chemicals.
• 42:22
  And this is out of Stanford University and Northwestern University led by Michael Jewett.
• 42:30
  Yeah, this one's a pretty interesting one.
• 42:32
  It's using like this cell-free system.
• 42:34
  So they remove all of the enzymes from a cell.
• 42:37
  They put it inside a test tube.
• 42:38
  And then what you can do is screen thousands, I think 3,000 enzyme variants to create what they call the reform pathway.
• 42:46
  And what it's doing is it's a synthetic system.
• 42:48
  So it's outside of a living cell and it's taking formate, which is a chemical compound that's derived from CO2 and converting that into acetyl coenzyme A.
• 42:58
  which is a universal molecule found in all living cells.
• 43:01
  It's part of our metabolism.
• 43:03
  Mitochondria, love it, so on and so forth.
• 43:05
  And it provides a path for sustainable carbon neutral manufacturing because if we want to take CO2 out of the atmosphere, we got to get clever.
• 43:13
  Maybe this is one of the ways that we do it.
• 43:15
  Mm-hmm.
• 43:15
  Mm-hmm.
• 43:16
  For those who are lost in the explanation, the analogy is imagine opening the hood of a car and using the engine to power a factory instead of just driving the car.
• 43:26
  Researchers are using the machinery of life in a test tube to build sustainable products out of thin air.
• 43:33
  Again, that's out of Stanford University and Northwestern University, Stanford.
• 43:38
  Story number three is a stem cell research story culturing the immune system's conductors.
• 43:47
  This is January 8th, 2026.
• 43:49
  Headline, UBC scientists solve the mystery of growing helper T cells.
• 43:55
  Now, if you've watched this show in the past, you know we've talked about the immune system a lot.
• 44:01
  So in our past episodes, we do cover this.
• 44:03
  But the logline is, for the first time, researchers have reliably produced T cells.
• 44:07
  helper T cells from stem cells, a major hurdle in creating off-the-shelf living drugs.
• 44:14
  And this is out of the University of British Columbia, led by Peter Zanstra and Megan Levings.
• 44:23
  Yeah, this one's interesting because, you know, we've been able to grow killer T cells, which are, you know, the soldiers of our immune system.
• 44:31
  But the helper T cells, which are kind of like the generals or like the kernels of the immune system, they're the ones that conduct all of the responses.
• 44:39
  They activate B cells to make antibodies.
• 44:42
  They trigger the macrophages, which are the eating immune cells that like eat up
• 44:48
  Bacteria and bad things.
• 44:50
  They conduct all of this stuff by releasing cytokines and all of these other important factors, but they're really hard to grow in a lab.
• 45:00
  And these guys precisely tuned a development signal called notch, and they were able to direct stem cells to become either type.
• 45:10
  Right.
• 45:10
  Killer or helper.
• 45:11
  Either killer or helper.
• 45:12
  Right.
• 45:12
  And now this allows for large scale manufacturing.
• 45:15
  Again, personalized drugs.
• 45:17
  You could take the stem cells from a patient and then make designed helper T cells that are specific to that specific patient.
• 45:26
  Yeah.
• 45:27
  It could be much more affordable.
• 45:29
  You know, you can customize patient specific therapies.
• 45:32
  It's very cool.
• 45:33
  So to the analogy that you brought up, we've already learned how to train soldiers to fight cancer, but we didn't know how to recruit the conductors who lead the orchestra on the battlefield.
• 45:44
  So this discovery finally gives us the full leadership team needed for a strong, lasting immune response.
• 45:53
  Our last rundown story of the day, as the UFO guy, you know we always have to...
• 46:01
  At some point, talk about exoplanet exploration, unmasking alien atmospheres.
• 46:07
  This is on January 12th, a conversation.
• 46:12
  NASA's Pandora Telescope will study stars in detail to learn about exoplanets orbiting them.
• 46:19
  Now, I'll just briefly note, this is not the last time we're going to be talking about planets.
• 46:25
  exoplanet exploration that's right we're not going to leak any classified information at this moment in time but for this story log line as the first mission of nasa's astrophysics pioneers program the pandora small sat will provide the first ever high precision tool for disentangling stellar noise from the actual atmospheric signal of distant worlds and we did talk about this in the episode where we covered the greatest evidence yet
• 46:50
  Yeah, K-218b.
• 46:52
  Of life on an exoplanet.
• 46:54
  But this story is out of the University of Arizona, led by Daniel Apai, NASA Goddard Space Flight Center and Ames Research Center, and the Lawrence Livermore National Lab and Cornell University.
• 47:07
  Yeah, this one's a cool one because it's quite a small satellite.
• 47:12
  The lens is only like 17 inches, which is pretty small compared to like Hubble or James Webb.
• 47:19
  But it's very specific.
• 47:21
  What it's trying to do is basically stare at the stars that have transiting planets.
• 47:26
  And what they want to do is disentangle when we see a planet going in front of the star and the sort of light coming from the star going down versus if the star has a sunspot and the sunspot has come this way.
• 47:40
  And now all of a sudden I'm just seeing less light because part of the star is dark.
• 47:43
  Right.
• 47:43
  Right.
• 47:44
  Right.
• 47:45
  It's very hard to disentangle the two, but if you just stare at something for a long enough time, you'll have an averaged out version of what that star should look like.
• 47:55
  And then you'll have a much better idea of what the planet looks like when it goes in front of the star.
• 48:01
  Yes.
• 48:02
  You can disentangle those two aspects.
• 48:04
  Yes.
• 48:04
  It's only in charge of 20 planetary systems.
• 48:07
  It's got...
• 48:08
  a year's worth of really yeah it's it's it's kind of like a pilot project in some sense but not really because you know even 20 getting that understanding for just 20 at that precision is going to be vital for interpreting data from larger missions like the james webb space telescope you can use that understanding for how stars work and then now start applying it to when the james webb looks at some something right mm-hmm mm-hmm
• 48:32
  This goes back to our point we always talk about, which is all of these discoveries and these movements are cumulative.
• 48:40
  They're not happening in a vacuum.
• 48:42
  So the immediate thing I just thought of was Vera Rubin and the massive amount of data that's going to be there in combination with JWST and some of the instruments that are looking at exoplanet atmospheres.
• 48:53
  There are data sets that have answers that we already have in our possession.
• 48:58
  Some are proprietary, some are open source, whatever.
• 49:03
  Once we find an unlock, all of a sudden makes these data sets incredibly valuable.
• 49:08
  And this is sort of maybe a scout type of mission.
• 49:10
  Yeah.
• 49:11
  To be able to create some of that better understanding, to get rid of the baseline of the planet.
• 49:15
  Exactly.
• 49:17
  And so that's, again, the exoplanet stuff I'm always such a huge fan of.
• 49:23
  We are going to come back into our main story number two of three.
• 49:30
  And this one is going to be an interesting combination.
• 49:34
  It's neuroscience, sleep, and marine biology.
• 49:38
  Yes.
• 49:39
  This is in Nature Communications, published on January 7th.
• 49:43
  Why do jellyfish need sleep?
• 49:47
  The ancient origins of our nightly rest.
• 49:49
  This is from Bar Ilhan University.
• 49:53
  The hook here is imagine a creature without a brain.
• 49:55
  Yep.
• 49:56
  That sleeps just like you.
• 49:58
  And scientists have discovered that jellyfish do exactly that.
• 50:02
  Do exactly that.
• 50:03
  They have no brain, but they sleep like us.
• 50:05
  Yes.
• 50:07
  It's a core paradox in biology, to be honest.
• 50:10
  It's like, why do we sleep?
• 50:12
  It's a very simple question, but if you think about it from an evolutionary point of view, it seems naively really dumb to do.
• 50:20
  Okay?
• 50:21
  Because sleep is a state, there's behavioral vulnerability, you're not aware of stuff, something could come and kill you.
• 50:30
  You're vulnerable to predation.
• 50:32
  It ceases foraging.
• 50:33
  You're not getting food.
• 50:34
  And also you're not having sex.
• 50:36
  So you're not really procreating.
• 50:38
  Yes.
• 50:38
  Right?
• 50:38
  So what's the point?
• 50:40
  And yet it's conserved across 600 million years of evolution.
• 50:44
  We have whales doing it.
• 50:45
  Humans do it.
• 50:47
  And now we've got jellyfish do it.
• 50:48
  Right?
• 50:49
  And the traditional view was it's a neurocentric view, meaning it has to do with complicated neuro...
• 50:56
  mechanisms, right?
• 50:57
  This idea of the synaptic homeostasis hypothesis, which is the idea that over the day, your neurons are constantly learning, and your synapses are growing.
• 51:07
  And if you just let it just keep growing and growing and growing, then you're going to get this runaway interaction, kind of like last episode we were talking about in DeepSeek, right?
• 51:16
  If you have these matrices that just
• 51:18
  start blowing up, then you're going to get a very chaotic system.
• 51:23
  So you need some way to constrain them.
• 51:24
  And maybe sleep was one way to constrain our synapses such that the signals don't propagate in a chaotic way.
• 51:32
  But now we've got this new finding, which shows that sleep is actually a fundamental cellular requirement for...
• 51:38
  because of DNA repair.
• 51:40
  And it's happening even in these brainless basal metazoans.
• 51:44
  So in this case, jellyfish, but they also did the experiments on sea anemone and they found very similar results.
• 51:51
  Okay.
• 51:51
  So let's get into some of the historical context for sleep research.
• 51:55
  Pre-1980s, there was this guy Hans Berger in 1929.
• 51:58
  He invents the EEG, which is the electroencephalogram.
• 52:02
  And what he showed was that people that are sleeping, they've got these cortical oscillations, alpha waves, and other types of brain waves.
• 52:11
  And he said, okay, sleep is basically the state when we get that.
• 52:16
  Right.
• 52:16
  All right.
• 52:17
  Now, that's fine, but that totally excludes invertebrates because invertebrates don't—I can't do an EEG on an invertebrate, right?
• 52:25
  I need some kind of centralized brain.
• 52:27
  So I can do this on mammals, but are you saying that, like, you know—
• 52:31
  lower order organisms don't sleep.
• 52:35
  So in the 1980s, there was this neuroscientist, Irene Tobler, at the University of Zurich.
• 52:40
  She came up with the behavioral criterion, okay?
• 52:44
  She showed that actually insects sleep, scorpions sleep, and there were three things that she wanted to classify something as sleep.
• 52:53
  She was a pioneer in her field.
• 52:54
  The three things are the following.
• 52:56
  First...
• 52:57
  Reversible immobility That makes sense, right?
• 52:59
  When we sleep, we're still-ish But somebody can wake us up So that's the reversible part The second one is increased arousal threshold If I poke you when you're awake You can sense it But if I poke you when you're asleep You probably can't I need to poke you really hard in order to wake you up
• 53:18
  Okay?
• 53:19
  Like, I need to, like, slap you.
• 53:21
  And then you're going to wake up.
• 53:22
  The amount of effort needed to elicit a behavioral response is higher.
• 53:28
  Is higher when you sleep.
• 53:29
  Okay.
• 53:30
  And the third thing is homeostatic regulation.
• 53:33
  This means that if I deprive you of this sleep, then the next time you sleep, you're going to sleep longer.
• 53:39
  We've all had this, right?
• 53:40
  Yes.
• 53:40
  Like, if you've had a night out...
• 53:43
  The next day, you're, quote, hungover.
• 53:45
  But what you're really doing is homeostatic regulation on top of the fact that you don't have any electrolytes.
• 53:50
  But, you know, you're sleeping way longer than you should be.
• 53:53
  Yes.
• 53:53
  Yes.
• 53:54
  So those are the three sort of behavioral definitions that I need in order to characterize sleep.
• 54:00
  I don't think I've ever thought about how do you define sleep before in my life.
• 54:03
  But that's interesting.
• 54:04
  It becomes obvious when you talk about humans.
• 54:06
  You can just ask them, like, did you sleep?
• 54:08
  With mammals, again, you could hook up an EEG.
• 54:11
  But now when we're getting into something like jellyfish...
• 54:14
  It's like, how do we define sleep for an organism that's just doing this the whole time?
• 54:19
  Right?
• 54:19
  Yes.
• 54:20
  But these three criteria, actually, we can translate.
• 54:24
  Okay?
• 54:24
  So the model organisms in this paper are niderians, jellyfish, and sea anemone.
• 54:31
  Okay?
• 54:32
  It's a paper that came out of Nature Communications.
• 54:35
  And these organisms actually diverged from the bilaterals, which are us, the...
• 54:41
  the bilateral meaning to the symmetric kind of thing instead of the round symmetry, they diverged from us 600 million years ago.
• 54:50
  It's even thought that maybe the nervous system of these Niderians is completely separately evolved from us.
• 54:57
  There's a little bit of debate on that because if you look at the neurons of these guys, the genes that they expressed are very similar to the genes that our neurons express.
• 55:07
  So perhaps there's a little bit of...
• 55:09
  Independence, but also at the same time, a little bit of the same genes going forward.
• 55:14
  But what's very cool is these guys don't have a brain.
• 55:17
  They have nerve nets.
• 55:19
  There's no centralized repository.
• 55:22
  Okay.
• 55:22
  It's a bunch of nerves that are just in a net together and they're making decisions completely.
• 55:28
  together and it's very unclear how they're doing okay but in any case very different from our centralized nervous system that we have with a central hub and it branching out to create motor functions there's a decision making that happens in our brain and then the rest of the neurons carry it out here we've got a very decentralized decision making process is this the idea of like there's like no executive function in these uh
• 55:53
  Yeah, it's very related.
• 55:55
  There's not an authority who decides and then everyone else has to follow.
• 55:59
  That's right.
• 55:59
  Yeah.
• 56:00
  Put it.
• 56:00
  Okay.
• 56:01
  Very, very much so.
• 56:02
  So the challenge now becomes, okay, we've got these two organisms that we want to study.
• 56:09
  Let's try to define sleep for them, right?
• 56:11
  Using those three criteria.
• 56:12
  Okay.
• 56:13
  So what they did was just make videos of these organisms in their natural, not natural habitat, but in lab conditions.
• 56:21
  They used infrared light because if you use normal light, then you're just messing with their rhythm, right?
• 56:27
  It's like you still need a night and day cycle.
• 56:29
  So instead you use high resolution infrared light.
• 56:32
  So this is infrared light.
• 56:33
  And here you see the jellyfish that are pulsating, right?
• 56:36
  This is Cassiopeia andromeda, the jellyfish.
• 56:40
  And what they could do is analyze these videos and figure out, basically classify two behavioral states based on those three rules that we had about what sleep is.
• 56:52
  They did the same thing for the starlit sea anemone.
• 56:56
  That's the nematostella vectenis.
• 57:01
  These are like Hydra-looking things.
• 57:03
  This is a 20-minute time lapse.
• 57:05
  They're much slower moving, so it's actually harder to classify immobility.
• 57:10
  But what you could do is actually classify the – even when they're awake, they have a little bit of jitter, and they could really hone in on that jitter and classify immobility.
• 57:20
  Rest, just five minutes of rest versus actual sleep.
• 57:24
  Sleep.
• 57:24
  Okay.
• 57:25
  Oh, interesting.
• 57:26
  And that's what was really cool about how they did this, right?
• 57:29
  They actually figured out a way to move beyond just these arbitrary dimensions and used threshold-based optimization algorithms that were custom-made for this use case, right?
• 57:41
  Yes, yes.
• 57:41
  Yes.
• 57:42
  And it's a rigorous definition.
• 57:43
  Yes.
• 57:44
  So they came up with these rigorous definitions.
• 57:46
  And now let's go ahead and try to analyze the sleep.
• 57:50
  When you analyze the sleep, one thing that's really cool is the Cassiopeia Andromeda, which is the jellyfish.
• 57:55
  Yes.
• 57:56
  Yeah.
• 57:56
  That's the one that actually it's so in the light phase, it was highly active.
• 58:00
  So during the day, it's very active.
• 58:01
  That's the high on the Y-axis.
• 58:03
  And then during dark times, it was very inactive.
• 58:06
  But what's very cool is during the day, right in the middle, it takes a siesta.
• 58:11
  It takes a midday nap.
• 58:12
  They're very European, I see.
• 58:14
  Yeah, yeah.
• 58:14
  These jellyfish are Spanish, in fact.
• 58:17
  In fact.
• 58:18
  Yeah.
• 58:20
  So these jellyfish, just like us, we like taking naps.
• 58:23
  These jellyfish like taking naps.
• 58:25
  The...
• 58:27
  Sea anemone, on the other hand, they were actually more active during dusk and dawn, and then they slept mainly during morning.
• 58:33
  And that could have something to do with the fact that their feeding has to do with tides, right?
• 58:37
  Because the water is moving in and out.
• 58:39
  So perhaps you want to be more active during the dusk and dawn, and then you can be less active when the sun is high in the sky and when the sun is low in the sky.
• 58:47
  Which kind of maps onto nocturnal versus non-nocturnal.
• 58:50
  Yeah, and speaking of nocturnal versus non-nocturnal, they also had the melatonin.
• 58:55
  That was affecting them.
• 58:56
  So melatonin, you know, that's the very famous compound that we can take in order to induce sleep.
• 59:00
  Yes.
• 59:01
  They tested it with 100 micromolar melatonin.
• 59:04
  And the melatonin promoted sleep in both the diurnal jellyfish and these anemone.
• 59:12
  So you're saying that someone's going to make billions of dollars selling melatonin 10 milligram, 100 milligram, whatever packages to see like Nemo.
• 59:20
  Yeah.
• 59:21
  Nemo, tell your dad.
• 59:22
  Yeah.
• 59:23
  We're coming with the melatonin.
• 59:24
  Yeah, exactly.
• 59:25
  Yeah, there's a huge market there, dude.
• 59:29
  Yeah.
• 59:30
  So now we've established that they sleep and the sleep is like pretty close to how we do it.
• 59:35
  And we saw it both in a natural context and by trying to induce it.
• 59:38
  Yes, exactly.
• 59:39
  So now the big question is why are these guys sleeping so much?
• 59:43
  Mm-hmm.
• 59:43
  OK, if they have no brains, if they've got no brain, the way we the way we conceptualize, yeah, the way we conceptualize brains, they don't have these like synaptic connections that they need to regulate up and down.
• 59:54
  So why are they sleeping so much?
• 59:56
  And this is the key significant contribution of this paper.
• 1:00:01
  OK, they're tying the sleep not to neural activity, but to cellular activity and the idea that at the molecular level, all cells need to recover.
• 1:00:15
  Oh.
• 1:00:15
  Because at the molecular level, what's happening with activity is activity means high entropy, right?
• 1:00:20
  There's high metabolic flux.
• 1:00:22
  The mitochondria are going crazy.
• 1:00:23
  They're creating ATP.
• 1:00:25
  Now, that's going to create a lot of reactive oxygen species, okay?
• 1:00:30
  And these reactive oxygen species are going to cause damage to the DNA backbone, okay?
• 1:00:36
  If you remember from our DNA episode, the DNA has this phosphate backbone, right?
• 1:00:40
  The reactive oxygen species that come as a byproduct of mitochondrial metabolism is going to start attacking this DNA backbone.
• 1:00:48
  And if you ever get something like a double-stranded break, that's really bad.
• 1:00:53
  Okay?
• 1:00:54
  And what they could actually quantify is during wakefulness, the double-stranded break was happening more often.
• 1:01:00
  Okay?
• 1:01:01
  So literally the process of living, of being awake and active is not only impacting your body in the physical sense that we think about it today.
• 1:01:16
  Mm-hmm.
• 1:01:17
  But the reason that our body needs sleep and rest is because at the DNA level, it's causing damage.
• 1:01:25
  It's causing damage.
• 1:01:26
  And that's because, again, I always thought, like, from my layman's perspective, like, oh, we sleep because your brain is tired.
• 1:01:34
  Yes.
• 1:01:34
  Like that was the idea.
• 1:01:36
  It's like you need to give your brain a rest.
• 1:01:37
  You're going to go crazy.
• 1:01:38
  Yeah.
• 1:01:39
  But really what this is identifying is that the destructive property that happens from lack of sleep is a degradation of your underlying DNA.
• 1:01:47
  Yeah, yeah.
• 1:01:48
  And for higher order organisms, it could be that the brain is also tired and the synaptic homeostasis model works, right?
• 1:01:55
  And that could be a thing.
• 1:01:56
  Yes.
• 1:01:56
  And it most definitely is.
• 1:01:57
  There's a lot of papers that do that.
• 1:02:03
  We needed sleep because fundamentally the act of living, as you said, degrades the molecular structure of our genetics.
• 1:02:12
  Right.
• 1:02:12
  And what they could do is show they could actually tag phosphorylated histone.
• 1:02:17
  And this phosphorylation happens at double stranded breaks.
• 1:02:20
  And they could track that actually the longer you're awake, the more double stranded breaks you're having.
• 1:02:25
  OK. And if you kept these organisms awake, the breaks would actually build up.
• 1:02:31
  The craziest thing to me was they could actually use that damage to drive sleep.
• 1:02:38
  So you know that homeostatic regulation that I was telling you about?
• 1:02:41
  Like, okay, if I deprive these animals of sleep, then they want to sleep longer.
• 1:02:46
  Well, I could actually artificially make double-stranded breaks happen, expose them to UV radiation.
• 1:02:53
  Now I have a bunch of DNA damage.
• 1:02:55
  The animals would sleep more.
• 1:02:58
  Because of more UV radiation, because there was more double-stranded breaks.
• 1:03:01
  So it's a direct causation now.
• 1:03:04
  It's not just a correlation.
• 1:03:05
  You've established a direct causation between DNA damage and more sleep.
• 1:03:10
  That's fascinating.
• 1:03:10
  Very cool.
• 1:03:11
  They artificially triggered DSBs, double-stranded breaks,
• 1:03:17
  Yeah.
• 1:03:18
  After having already monitored natural behavior.
• 1:03:20
  Yep.
• 1:03:21
  And it triggered an increase in the amount of sleep in both the jellyfish and the sea anemone when they were exposed to an external factor that one of the primary things it did was create a larger volume of double-stranded breaks.
• 1:03:37
  Yeah, exactly.
• 1:03:38
  Or degradation of the molecular DNA stuff.
• 1:03:40
  And they didn't comment on this, but I've always felt that, like, if I'm out in the sun for really long, I need a nap.
• 1:03:46
  I need a nap.
• 1:03:47
  I don't know if— I'm not saying that the authors— I'm just saying it's, like, kind of— I don't know.
• 1:03:54
  Look, it is Southern California, so we do know what it's like to be out in the sun a lot.
• 1:03:58
  I feel like when I get sunburned— And I do get sunburned, okay?
• 1:04:01
  Like, even though, like, I'm brown, I do get sunburned.
• 1:04:04
  And then when I do, it's like I do like I end up napping for like two hours and it's the best now.
• 1:04:09
  It's the best.
• 1:04:10
  It's usually the best.
• 1:04:10
  Yeah.
• 1:04:11
  Yeah.
• 1:04:11
  It's usually.
• 1:04:12
  And, you know, maybe that's like, you know, the DNA repair being like, finally, we get to, like, fix all this nonsense that he's been up to.
• 1:04:20
  This is good.
• 1:04:21
  And again, this is like, again, a story at the intersection of spaces I didn't expect to sort of see.
• 1:04:27
  Right.
• 1:04:28
  You have it was like DNA damage.
• 1:04:30
  OK.
• 1:04:30
  The neuroscience story because we thought that sleep was about the brain only.
• 1:04:35
  Again, we've caveated.
• 1:04:36
  This is not saying that sleep is not involved in neurological activity.
• 1:04:42
  It's just that we can see that it is not the only brain.
• 1:04:45
  thing that it's involved in.
• 1:04:47
  Yeah.
• 1:04:47
  And it's like an invariant across animals that have neurons.
• 1:04:50
  And one of the cool things is when you think about like, why would neurons specifically need sleep?
• 1:04:55
  Yeah.
• 1:04:56
  Right.
• 1:04:57
  Neurons are organisms that don't divide.
• 1:05:00
  Right.
• 1:05:02
  neurons are not organisms.
• 1:05:04
  Neurons are cells that are post-mitotic.
• 1:05:06
  They don't do mitosis.
• 1:05:07
  They don't cell divide.
• 1:05:09
  So once you have them, you have them.
• 1:05:10
  So maintaining the cell state of a neuron is way more important than, let's say, skin cells.
• 1:05:14
  Skin cells, if you've got something that's bad, you shed it, you make new ones, because those things are going through mitosis all the time.
• 1:05:20
  Muscle cells as well.
• 1:05:21
  They're replicating all the time.
• 1:05:23
  Neurons, on the other hand, you got what you got.
• 1:05:25
  And so you need to maintain it, right?
• 1:05:27
  Which is why animals with neurons...
• 1:05:31
  might require sleep because DNA damage in a neuron is way more important to fix than anything else.
• 1:05:38
  And so in order to protect your limited neurons, make sure you share the pod with a friend because what better way to stretch out and exercise those neurons than listening to the From First Principles podcast.
• 1:05:51
  It's for your brain health.
• 1:05:52
  Yes.
• 1:05:52
  So if they ask you, this podcast helps my mental health, my biological mental health,
• 1:05:59
  Yeah.
• 1:05:59
  Stretching and pulling and keeping flexi.
• 1:06:02
  And then get some sleep right after because then that'll actually, studies have shown that, you know, sleep actually consolidates that memory into long-term storage.
• 1:06:09
  So you might actually remember something that we're talking about.
• 1:06:13
  It's for your health.
• 1:06:14
  That's why you listen to the pod.
• 1:06:16
  Great story number two.
• 1:06:17
  We started with string theory.
• 1:06:18
  Yeah.
• 1:06:19
  Then we went into this sort of biology, neuroscience, sleep story, understanding a little bit more about sleep having a larger scope of value to other sort of animals within the animal kingdom.
• 1:06:36
  And we're going to end this episode with a space story.
• 1:06:42
  Yes.
• 1:06:44
  Story number three is about dark matter.
• 1:06:48
  Yes.
• 1:06:50
  Dark Matters, First Frontier, and the discovery of Cloud 9.
• 1:06:55
  Not that Cloud 9.
• 1:06:58
  Different cloud net.
• 1:06:58
  Or is it?
• 1:06:59
  Or is it?
• 1:06:59
  I don't know.
• 1:07:00
  I don't know how it was named, honestly.
• 1:07:02
  We will get to that question.
• 1:07:03
  But astronomers may have found the first example of a primordial gas cloud trapped in a dark matter halo, offering a rare glimpse into how the universe's first structures formed billions of years ago.
• 1:07:18
  This is coming in the Astrophysical Journal Letters on January 7th from the Space Telescope Science Institute,
• 1:07:27
  The Universita D'Aglia Studi di Milano Bioka, University of Victoria.
• 1:07:35
  And another placement, I think they might be third place in our stories, of the University of Wisconsin.
• 1:07:41
  Wisconsin is doing well in our stories.
• 1:07:44
  They are performing very, very highly.
• 1:07:47
  So what makes Cloud9, the new celestial discovery, a cosmic game changer for star formation theories?
• 1:07:55
  Yes.
• 1:07:55
  Yeah, so they're claiming, you know, it's been all over the news, they're claiming that this is a new type of object, okay?
• 1:08:03
  It's a compact, gas-rich, starless object.
• 1:08:08
  And...
• 1:08:09
  It's a totally different thing in our cosmic zoo.
• 1:08:12
  You know, we got galaxies, we got stars, we got planets, and now perhaps a totally different animal altogether.
• 1:08:20
  Okay.
• 1:08:20
  Okay?
• 1:08:21
  That's very rare.
• 1:08:22
  Because we've been staring at the night sky for a while.
• 1:08:25
  Right, right.
• 1:08:25
  So a new type of object is quite interesting, right?
• 1:08:29
  Yes.
• 1:08:30
  That definitely doesn't fit into another bucket.
• 1:08:32
  Yes, yes.
• 1:08:33
  And what's really important about this new type of object is it's confirming a lot of
• 1:08:37
  Theories about dark matter that we have.
• 1:08:40
  There were some holes in that theory that this is sort of filling.
• 1:08:44
  And at the same time, it's raising some new questions as almost all new stuff does.
• 1:08:49
  Yes.
• 1:08:49
  Right.
• 1:08:49
  So let's start at the very beginning.
• 1:08:50
  We're going to start at the Big Bang and we're going to start with something called Lambda CDM.
• 1:08:54
  In the Beninging.
• 1:08:55
  Yes.
• 1:08:56
  There it is.
• 1:08:57
  Lambda CDM is cold dark matter.
• 1:08:59
  That's the prevailing cosmological model that we have for all the stuff that is in our universe.
• 1:09:06
  Not a lot of normal matter.
• 1:09:08
  a lot of dark matter.
• 1:09:09
  So I think 85% of stuff that is gravitationally influenced is dark matter.
• 1:09:16
  Only 15% is normal matter.
• 1:09:18
  And then we don't have to talk about dark energy, which is a whole other thing.
• 1:09:22
  That's actually even more than dark matter, right?
• 1:09:24
  But let's not get into that.
• 1:09:25
  Let's just talk about stuff that is influenced by gravity.
• 1:09:28
  So in the very beginning, there was the Big Bang.
• 1:09:30
  The Big Bang expanded into
• 1:09:35
  It didn't expand into space.
• 1:09:36
  Space itself expanded.
• 1:09:37
  Let me catch myself when I say that.
• 1:09:39
  Right?
• 1:09:40
  The comets would have gone crazy with that one.
• 1:09:42
  Yeah, yeah.
• 1:09:42
  So 300,000 years after the Big Bang, we formed atoms.
• 1:09:46
  That's when we got our cosmic microwave background.
• 1:09:48
  And then we had our first stars, our first galaxies, our first black holes.
• 1:09:53
  And through all of this, dark matter was also expanding with all of the baryonic matter.
• 1:09:58
  That's the ordinary matter, stuff of atoms, quarks, electrons, things like that.
• 1:10:04
  And at some point, the first stars began collapsing.
• 1:10:10
  They started blowing up.
• 1:10:12
  The black holes started creating a mess, right?
• 1:10:17
  They started creating ultraviolet radiation.
• 1:10:20
  And all of this time, what was happening was this ultraviolet radiation was heating up the intergalactic medium, right?
• 1:10:28
  to a temperature of approximately 10 to the 4, so 10,000 Kelvin, 10,000 degrees Celsius.
• 1:10:35
  Okay?
• 1:10:35
  Pretty cool.
• 1:10:36
  Pretty cool.
• 1:10:36
  And this was happening around 400 million years after the Big Bang.
• 1:10:40
  This is called the reionization epoch.
• 1:10:43
  Because what's happening now is all of the hydrogen that's in our intergalactic medium is getting bathed in this UV radiation.
• 1:10:50
  Okay?
• 1:10:51
  And this whole time, dark matter is just doing its thing.
• 1:10:54
  So all of the hydrogen, which is the baryonic matter, is getting bathed in UV radiation.
• 1:10:58
  Meanwhile, the UV radiation is not at all interacting with the dark matter.
• 1:11:02
  That's the point of dark matter.
• 1:11:04
  It does not interact with light, hence it's dark.
• 1:11:07
  So dark matter has been collapsing this whole time.
• 1:11:10
  And when dark matter collapses, it forms clumps.
• 1:11:15
  These are called halos.
• 1:11:16
  And the clumps happen at all different scales.
• 1:11:20
  If we look out at the dark matter halo function, the mass function that we see today, that function is something that we call scale-free.
• 1:11:29
  So instead of the size of the dark matter halos sort of collapsing as an exponential, where there's some like...
• 1:11:36
  fundamental scale constant of that exponential this thing is a power law which means as we get to smaller and smaller scale like scales of halos there should be more and more of these dark matter halos so there should be a halo for the milky way that is as big as the milky way and at the center of this dark matter halo is the our normal milky way but then around this milky way there should be a bunch of different tiny clumps and around those tiny clumps there should be even smaller
• 1:12:07
  referential fractally system.
• 1:12:10
  It's like the Russian dolls.
• 1:12:11
  Yes, exactly.
• 1:12:12
  But the smaller the Russian doll, the more there should be.
• 1:12:15
  That's key.
• 1:12:16
  Okay.
• 1:12:16
  Okay.
• 1:12:17
  Yes.
• 1:12:17
  Yeah.
• 1:12:18
  Okay.
• 1:12:18
  Interesting.
• 1:12:19
  Okay.
• 1:12:19
  So now let's think about what would happen with these halos, right?
• 1:12:27
  The bigger the halo, there should be galaxies inside, right?
• 1:12:30
  We don't actually see all that many halos.
• 1:12:34
  That's kind of a problem.
• 1:12:36
  OK, yeah.
• 1:12:36
  Now, there could be a solution about why we don't see a bunch of small halos.
• 1:12:41
  OK, and that has to do with the nature of these halos and the normal matter that's inside of these halos.
• 1:12:47
  So let's talk about something called hydrostatic equilibrium.
• 1:12:51
  Normally, we think about hydrostatic equilibrium in the sense of why are stars stable?
• 1:12:56
  Like, why isn't every star a black hole?
• 1:12:59
  Because if gravity is just pushing everything inside, then everything should just collapse into a black hole.
• 1:13:04
  Well, the reason is there's a bunch of fusion going on, and that fusion creates an external pressure outward that is counteracting the gravitational pressure.
• 1:13:13
  And that's what keeps a star...
• 1:13:15
  Stable.
• 1:13:16
  Yes.
• 1:13:16
  Okay, now let's think about dark matter halos.
• 1:13:18
  Okay.
• 1:13:19
  Okay, with dark matter halos, remember I told you that during reionization, this cosmic dawn is what they call it, when there was a bunch of ultraviolet radiation, that ultraviolet radiation is going to heat up
• 1:13:29
  normal matter.
• 1:13:31
  So that normal matter is inside of a gravitational well of dark matter.
• 1:13:35
  So it's getting pushed in because of gravity.
• 1:13:37
  But it's also hot.
• 1:13:39
  And the hot is going to push out.
• 1:13:42
  And so there itself is a kind of hydrostatic equilibrium.
• 1:13:45
  The hotness is not coming from fusion.
• 1:13:47
  It's just coming from being bathed by this early ultraviolet light.
• 1:13:52
  Got it.
• 1:13:52
  Understood?
• 1:13:52
  Yes.
• 1:13:53
  Okay.
• 1:13:53
  So there we get into a very cool thing.
• 1:13:59
  which is there's actually a size limit to how big a dark matter halo can get before it starts having stars inside of it.
• 1:14:08
  Because imagine a very large dark matter halo, okay?
• 1:14:11
  It's got a bunch of matter inside.
• 1:14:13
  The very large dark matter halo is going to start collapsing.
• 1:14:16
  The matter inside is big enough that it's going to start collapsing and it's going to start creating galaxies.
• 1:14:21
  But if it's really small...
• 1:14:23
  The matter inside is just going to sort of evaporate out because the hotness is too big.
• 1:14:29
  Sorry, the hotness is too hot.
• 1:14:31
  The particles are moving too fast for the gravitation of that small, tiny little dark matter halo to keep everything inside.
• 1:14:38
  So there comes this critical mass threshold.
• 1:14:42
  And they calculate it, the paper actually quotes it to be around 10 to the 9.7 times the mass of the sun.
• 1:14:48
  The Milky Way for context is around 10 to the 12.
• 1:14:51
  So this is much smaller than the Milky Way.
• 1:14:54
  But at that critical mass threshold, the dark matter halo is...
• 1:15:00
  big enough where it's like keeping the stuff inside.
• 1:15:05
  Okay.
• 1:15:05
  Anything bigger and the stuff that's inside is going to coalesce to become a galaxy and become stars, anything smaller and it's not enough.
• 1:15:12
  And the, and the gas is just going to leave.
• 1:15:14
  But right at that threshold, we've got this sweet spot where the gas is going to retain inside of my halo, but it's not enough to create stars and galaxies.
• 1:15:26
  Okay.
• 1:15:27
  You see?
• 1:15:27
  Yes.
• 1:15:27
  There's a sweet spot.
• 1:15:30
  It is this buffer line where it can just contain a lot of...
• 1:15:35
  This is why I'm thinking about...
• 1:15:37
  This is why the cloud metaphor becomes interesting here.
• 1:15:40
  Because it's at a threshold where it can't materialize... Like this physical large-scale matter the way we think about it.
• 1:15:47
  And it's just these gas particles all... Yeah, just moving around...
• 1:15:50
  Hanging out.
• 1:15:51
  Hanging out, yeah.
• 1:15:52
  And so there was a term that was coined in 2017 in monthly notices.
• 1:15:57
  It was called the RELHIC, which is, I think it stands for Reionization Limited H1 Clouds.
• 1:16:03
  H1 meaning neutral hydrogen, okay?
• 1:16:06
  It was a paper out in monthly notices, and what they said was there should be these halos right at the critical mass, right?
• 1:16:12
  That should be massive enough to retain neutral gas, neutral hydrogen gas, but not massive enough to cool down and condense and form stars.
• 1:16:22
  Yes.
• 1:16:23
  Okay.
• 1:16:23
  Yes.
• 1:16:24
  And these are called relics.
• 1:16:26
  So finding one of these relics would be awesome.
• 1:16:29
  I just want to quickly pause and say back engineering.
• 1:16:33
  I can't remember what the name for it was.
• 1:16:35
  Back engineering, the acronym from what you want it to sound like.
• 1:16:38
  relics i mean it's clever it's a good one scientists do this all the time so relics is a really good one reionization limited h1 clouds that's pretty good okay so finding these relics would be really awesome yes but finding it is very hard because this thing has no stars right right so how do the whole point like how do we see something we see it using light but this thing has no stars
• 1:17:02
  Dark matter is invisible.
• 1:17:03
  Yeah, right.
• 1:17:04
  Which is what makes any kind of discovery around this subject of dark matter extremely difficult.
• 1:17:09
  Yes.
• 1:17:09
  Because we are only as good as the instruments we build in order to understand the world around us, and we have not yet...
• 1:17:16
  Maybe.
• 1:17:17
  Yeah.
• 1:17:17
  Figured out a lot of this, but maybe.
• 1:17:19
  Maybe.
• 1:17:20
  Maybe.
• 1:17:20
  And what comes to the rescue is the hydrogen 21 centimeter line.
• 1:17:24
  Yes.
• 1:17:24
  Okay.
• 1:17:25
  This is our favorite line.
• 1:17:28
  Yes.
• 1:17:28
  1420 megahertz.
• 1:17:29
  Yes.
• 1:17:29
  This is the hyperfine transition.
• 1:17:31
  This was used in the Pioneer plaque to tell...
• 1:17:35
  Aliens, what our standard time and our standard unit of measurement is for all of the, you know, diagrams that we have on the Pioneer plaque in case the aliens found the Pioneer plaque and they wanted to see where Earth was.
• 1:17:47
  All of the standard measurement on that Pioneer plaque is made using the hyperfine transition of hydrogen, which is effectively hydrogen has a proton and an electron.
• 1:17:57
  If they're spinning parallel to one another...
• 1:18:00
  Versus when they're spinning opposite to one another, that difference in energy corresponds to a particle of light, a photon, that is exactly the wavelength 21 centimeters or around 1420 megahertz, right?
• 1:18:15
  Sidebar.
• 1:18:17
  At Princeton Physics, one of my favorite classes was the undergraduate advanced physics lab, where we actually used the hydrogen 21 centimeter line to map out the Milky Way.
• 1:18:29
  There was a radio telescope on the roof of the physics department that we could use to point it in different directions, and we could point it...
• 1:18:38
  at the galactic disk of the Milky Way, and you could see the 21 centimeters, and you could actually figure out what part of the galactic disk was moving away from us versus moving towards us by pointing it in different directions, and that 1420 megahertz would be a little bit red shifted or a little bit blue shifted, depending on where you were looking.
• 1:18:57
  And what you could do, I mean, there's a lot of error, right?
• 1:18:59
  Because you don't know exactly where you're pointing.
• 1:19:01
  It's kind of a, you know, old disk that they were like, okay, let the undergrads
• 1:19:06
  you know, use it for their lab.
• 1:19:08
  So you don't know where it's pointing and the frequency resolution is not that great, but you could actually like, I remember calculating the rotation curve, which is depending on how far away I am from the galactic center.
• 1:19:19
  Um,
• 1:19:21
  how fast is the stuff moving?
• 1:19:23
  And you could actually chart out the galactic rotation curve and you could see that the galactic rotation curve plateaued.
• 1:19:32
  Now, okay, this is not as good as the data that I got.
• 1:19:34
  The data that I got had massive error
• 1:19:36
  bars and only like eight data points but what you could see is that it would rise and then it would plateau and this is characteristic of dark matter vera rubin the astronomer actually used these galactic rotation curves to calculate dark matter because if you think about it the farther away you get from a galaxy center the slower yes you should be moving right because newton's laws inverse
• 1:20:01
  Here, the farther you get away, you're still moving at the same rate.
• 1:20:05
  The only way this is possible is if there's a bunch of invisible matter that's actually pulling on you.
• 1:20:09
  Yes.
• 1:20:10
  That's creating this gravitational force.
• 1:20:12
  That's sustained.
• 1:20:13
  That creates that plateau of sustained speed because otherwise... Like, there's no other external factor that could be...
• 1:20:20
  Yeah, because we don't see any stars.
• 1:20:22
  And yet you're telling me that there's a bunch of new mass that's pulling on you.
• 1:20:26
  Because you're farther and farther away and your speed has not changed.
• 1:20:28
  Yeah.
• 1:20:29
  Even though it should now be slower because you're farther away from the gravitational center.
• 1:20:32
  Exactly.
• 1:20:33
  So that gravitational force must come from something.
• 1:20:36
  It has to come from something.
• 1:20:37
  And that's where sort of, okay, yeah, dark matter.
• 1:20:39
  It makes sense.
• 1:20:40
  There's a lot of other avenues of research that actually point to dark matter.
• 1:20:43
  But this is one of the really cool ways of showing, yeah, there's got to be something there that we're not seeing.
• 1:20:49
  And it's also, like, even for someone who's not as deep in this, it can be relatively intuitive.
• 1:20:53
  Yeah, yeah.
• 1:20:53
  And it's something that an undergrad can figure out, you know?
• 1:20:56
  Right.
• 1:20:56
  Using a shitty radio dish on the roof of Jadwin Hall.
• 1:21:02
  That was one of my favorite, like, two weeks was actually doing this experiment and then writing the report.
• 1:21:08
  Okay, so...
• 1:21:09
  Now let's get back to the story.
• 1:21:11
  How do we find these?
• 1:21:11
  Well, we look at the hydrogen 21 centimeter line, right?
• 1:21:14
  And the FAST radio telescope, which is the 500 meter aperture spherical telescope, in 2023, it found a region of space that looked kind of weird.
• 1:21:25
  This is the radio telescope.
• 1:21:26
  It's out in Guizhou, China, just north of Vietnam.
• 1:21:29
  It looks kind of like Arecibo, which was out in Puerto Rico.
• 1:21:33
  It got damaged by the hurricane and is no longer in operation.
• 1:21:36
  But now China has something that's even better.
• 1:21:39
  That pronunciation was pretty good.
• 1:21:41
  Where did you learn that from?
• 1:21:42
  I looked it up.
• 1:21:44
  I looked it up earlier.
• 1:21:47
  Yeah.
• 1:21:47
  So they've got this telescope, the Fast Radio Telescope.
• 1:21:51
  In 2023, it spots this object, okay?
• 1:21:53
  Yes.
• 1:21:53
  And this object has a bunch of 21 centimeter, 1420 hertz coming through.
• 1:22:01
  And from all of the previous surveys, there's nothing starry there.
• 1:22:05
  Right.
• 1:22:05
  Okay?
• 1:22:06
  So it's a patch of sky where we're getting a bunch of...
• 1:22:10
  hydrogen emission, but there's no real stars there.
• 1:22:12
  There's nothing there.
• 1:22:13
  It's right next to another galaxy, the M94 group, and the recession velocity of this gas, so if you were to look at the redshift of this gas, it's very similar to the recession velocity of 304 kilometers per second
• 1:22:26
  Of the galaxy.
• 1:22:27
  So it's associated to that galaxy because they're both moving away from us at the same speed.
• 1:22:32
  And they're both right next to each other.
• 1:22:33
  So they've got to be sort of tied together.
• 1:22:35
  Yes.
• 1:22:35
  Right.
• 1:22:36
  Okay.
• 1:22:36
  So that's the first observational signature, right?
• 1:22:39
  The second observational signature comes from the VLA, the very large array.
• 1:22:44
  Yes.
• 1:22:45
  This is very recent.
• 1:22:46
  I think it came out last year.
• 1:22:48
  It shows that the broadening of that hydrogen line...
• 1:22:55
  is very close to what we would expect if that hydrogen was thermalized to that UV radiation that I was talking about in the reionization epoch.
• 1:23:03
  Because imagine, right?
• 1:23:04
  I've got a cloud of hydrogen.
• 1:23:06
  It's hot.
• 1:23:07
  And the temperature is right about at 10 to the 4 Kelvin, which is what we calculate the UV radiation to be back then.
• 1:23:13
  And if the temperature is around that, then there's going to be some gas particles that are moving away from us, some gas particle moving towards us.
• 1:23:20
  And the spread...
• 1:23:21
  of that thermal radiation is going to be right around 12 kilometers per second if you calculate what the thermal radiation should be and that's exactly what we see the vla looks at that thing and sees that the spread is about 12 kilometers per second so again okay this is thermalized radiation yes from that realization epoch yes okay very good very good
• 1:23:42
  And also, if you look at rotating dwarf galaxies, those dwarf galaxies have a thermal radiation of like 30 to 50 kilometers per second, much faster than what we see.
• 1:23:52
  And this is where we get to the Astrophysical Journal letters paper that we're going to talk about.
• 1:23:56
  The main story is this paper that came out very recently.
• 1:24:00
  The first relic?
• 1:24:01
  Yeah.
• 1:24:02
  They're asking, is this cloud nine that we've been looking at for the past two or three years?
• 1:24:08
  Yes.
• 1:24:08
  Is this finally the thing that we had hypothesized in 2017?
• 1:24:13
  And actually, some of the authors of this paper were the authors on that original paper that hypothesized the relic.
• 1:24:20
  So this guy's been chasing after.
• 1:24:21
  Yes.
• 1:24:22
  A relic for like three or four years now.
• 1:24:24
  And he's finally trying to figure out, is this it?
• 1:24:26
  Yes.
• 1:24:27
  And what he's done is use the Hubble Space Telescope to point it at that thing and say there are no stars here.
• 1:24:34
  Right, right, right.
• 1:24:35
  Okay?
• 1:24:35
  Yes.
• 1:24:36
  Unequivocally.
• 1:24:37
  Because before we have like surveys and they might have missed something.
• 1:24:40
  But here we've got a Hubble Space Telescope mission that is looking at this thing.
• 1:24:44
  Right.
• 1:24:45
  for a very long time, okay?
• 1:24:47
  Almost five hours total.
• 1:24:49
  So it had two 9,000-second...
• 1:24:53
  exposures at that spot in the sky.
• 1:24:56
  And it's looking at it with two different filters.
• 1:24:58
  And those filters are optimized so that it looks for red giant branch stars.
• 1:25:03
  Because if there's a dwarf galaxy in there, that dwarf galaxy is probably going to be old and metal poor.
• 1:25:09
  So the brightest stars in there are going to be red giants.
• 1:25:11
  Okay.
• 1:25:12
  And the amount of exposure time that these guys used was,
• 1:25:16
  was enough to see stars that are much fainter than those red giants.
• 1:25:19
  So we're not going to miss it.
• 1:25:20
  Right.
• 1:25:21
  That's the point.
• 1:25:22
  If there's any there, we're not going to miss it.
• 1:25:24
  The threshold was high enough that it would have captured it.
• 1:25:26
  It would have captured it.
• 1:25:27
  It's like, oh, well, you didn't turn Cricket to 11.
• 1:25:29
  No, no, no.
• 1:25:29
  We stared at this thing for long enough.
• 1:25:31
  If there was something there, we would have seen it.
• 1:25:33
  And they only observed three sources.
• 1:25:35
  That they detected in that region.
• 1:25:37
  And then they said, okay, is three a lot?
• 1:25:38
  Maybe those three stars are, you know, what can I say?
• 1:25:42
  Is that a lot?
• 1:25:42
  Is that a little?
• 1:25:43
  Well, this is something that's very clever and something that people do in science all the time is they try to measure something and then they say, what could I see just by chance?
• 1:25:53
  What is my background?
• 1:25:54
  Okay.
• 1:25:55
  So they looked and they measured the density of sources in blank regions of that image that are far away from cloud nine.
• 1:26:01
  And what they found was the average background is about 3.7 for a similar region.
• 1:26:06
  So even just by chance, the amount of stuff that I would see in something that's not there is about 3.7.
• 1:26:13
  So the fact that I'm seeing three is kind of just a roll of the dice.
• 1:26:16
  Yes.
• 1:26:17
  You know?
• 1:26:17
  Yes.
• 1:26:18
  It's not out of...
• 1:26:22
  It's not out of the ordinary.
• 1:26:24
  The fact that there's three there is what all other background space looks like.
• 1:26:27
  Exactly.
• 1:26:28
  It's about three things there.
• 1:26:29
  Yeah.
• 1:26:29
  Yeah, exactly.
• 1:26:30
  So if I were to just, like, look at a random patch in the sky, I'd see three things in that region.
• 1:26:35
  But if there's a dwarf galaxy there, I should see a hell of a lot more than just three.
• 1:26:39
  Yes.
• 1:26:39
  Right?
• 1:26:40
  They also simulated what it would look like if there was a galaxy there.
• 1:26:47
  You see, okay, what if there's like this many, what if there's a dwarf galaxy that's this big?
• 1:26:52
  Okay.
• 1:26:53
  And they went all the way down to really small dwarf galaxy.
• 1:26:56
  Yes.
• 1:26:56
  And they said you would still be able to see something.
• 1:26:59
  Yes.
• 1:26:59
  Okay.
• 1:27:00
  If I do the simulation, I'd be able to see something.
• 1:27:02
  Yes.
• 1:27:03
  But I'm not.
• 1:27:03
  I'm only.
• 1:27:04
  We're seeing three in real life.
• 1:27:07
  If we simulate being very bright and very dim, we would see 25.
• 1:27:11
  Yeah.
• 1:27:12
  We'd see a lot more.
• 1:27:13
  But we're seeing three.
• 1:27:14
  Three.
• 1:27:16
  so it's like there is nothing there guys yes yes yes yes there's nothing there and a lot of this is it's trying to like remove it's trying to one provide a repeatable path to say you can look at the same patch of sky yeah and you can do the same math calculations and it's not a red dwarf this that and the third because we've done it and you can repeat it and you would get to the same yeah yeah it'd be hard to get time on the hubble space telescope again to look at the same patch of sky fair
• 1:27:41
  But, I mean, there's a ground-based telescope and things like that.
• 1:27:43
  So you could do it.
• 1:27:45
  And actually, one of the future things that you could do is maybe use the James Webb Space Telescope and look for that same patch of sky and get into an even deeper stellar mass limit, right?
• 1:27:55
  Maybe we're not seeing, like, brown dwarves or, like, really cool stars, right?
• 1:27:59
  Yes, yes.
• 1:28:00
  That's something that the James Webb telescope could do.
• 1:28:02
  Okay, so the next thing they did was derive the halo mass of this thing.
• 1:28:07
  And this is actually kind of cool because usually when we want to derive the halo mass, the dark matter halo that's in a galaxy, what we look at is stars.
• 1:28:16
  We say, okay, how fast is the star moving around?
• 1:28:19
  Yes.
• 1:28:19
  Based on that, I can calculate how much mass is inside the stellar orbit.
• 1:28:23
  Yes.
• 1:28:23
  And then from that, then I can extrapolate and say, okay, if there's this much mass inside the stellar orbit and the mass function of a galactic halo kind of falls off like this, then I can integrate over that entire volume.
• 1:28:36
  And then I could say, okay, the total mass of the halo is something like this.
• 1:28:41
  Here, I've got no stars.
• 1:28:42
  Yeah.
• 1:28:43
  So how do you...
• 1:28:43
  So how do I... What am I going to do?
• 1:28:45
  Right?
• 1:28:46
  Yes.
• 1:28:46
  So what they did was they went back to that hydrostatic equilibrium analogy and they said, okay...
• 1:28:51
  If I've got a gas temperature that's 10 to the 4 and I do it, I can calculate the mass.
• 1:28:58
  And the mass is somewhere close to 5 times 10 to the 9 solar masses, which is very close to the 10 to the 9.7 that they were showing earlier.
• 1:29:07
  So they calculate this critical mass, and it's right at that threshold, which is, again, very good because that's the whole point of that theory of like there's this critical mass.
• 1:29:16
  Now we've actually found this critical mass, and it matches up.
• 1:29:19
  Yes.
• 1:29:19
  It's unique.
• 1:29:20
  And just to clarify, the point being it's this critical mass where it is –
• 1:29:25
  not going to end up with stars forming because things collapse inwardly and it's not going to end up with dissipation because things are exiting because it's too hot.
• 1:29:34
  It's balanced.
• 1:29:34
  It's just balanced.
• 1:29:36
  And we're observationally seeing something that is at that line where there should be that balance.
• 1:29:42
  Which,
• 1:29:42
  And that's inevitably like it kind of is all pointing to this could be the relic.
• 1:29:47
  This could be the relic.
• 1:29:47
  Because it's in the Goldilocks zone for this dark matter halo concept with no matter inside but still able to maintain structure without dissipating or expanding.
• 1:29:58
  Exactly.
• 1:29:58
  Okay.
• 1:29:59
  Exactly.
• 1:29:59
  Yeah.
• 1:30:00
  And so now the question is how unique is this thing?
• 1:30:03
  Right.
• 1:30:04
  Right?
• 1:30:04
  Right.
• 1:30:05
  Because you're saying this is a new celestial object.
• 1:30:07
  Yes.
• 1:30:08
  That's a tall order.
• 1:30:09
  Yes.
• 1:30:09
  Okay?
• 1:30:09
  So here what they've plotted is on the x-axis the stellar mass of the thing.
• 1:30:14
  Yes.
• 1:30:15
  And then on the y-axis is the hydrogen mass.
• 1:30:18
  How much hydrogen does it have?
• 1:30:19
  Most of the stuff, like the dwarf galaxies, normal galaxies, are way to the right.
• 1:30:24
  The top to the right.
• 1:30:25
  Yeah.
• 1:30:25
  They're very big with a lot of hydrogen.
• 1:30:27
  Yeah.
• 1:30:27
  Yeah, because the point is, if you've got a lot of hydrogen, then usually you're going to have a bunch of stars.
• 1:30:34
  Okay?
• 1:30:34
  If you've got a lot of intergalactic hydrogen, usually you've got a bunch of stars.
• 1:30:38
  Here, you don't have a lot of stars, but you've got a bunch of hydrogen.
• 1:30:41
  That's why it's all the way to the left.
• 1:30:43
  And there's nothing like it.
• 1:30:44
  Right?
• 1:30:45
  It is.
• 1:30:45
  And for people who are listening, and we really do encourage you if you have the opportunity to watch the show when you can or even segments because the visuals really illustrate it.
• 1:30:54
  Right?
• 1:30:55
  This is like an order of magnitude.
• 1:30:57
  Yeah, no, it's actually two orders of magnitude because the axes are on a log scale.
• 1:31:03
  Ah, okay.
• 1:31:04
  Yeah.
• 1:31:04
  It is like so far away from anything else to the point of is it unique?
• 1:31:09
  And it's like, well, it doesn't look at them.
• 1:31:11
  Look, look, look, dude.
• 1:31:12
  It's like there's nothing around it.
• 1:31:15
  right?
• 1:31:15
  Yeah.
• 1:31:16
  This is a new type of object and it could be this relic that they've been trying to look for.
• 1:31:20
  Yes.
• 1:31:20
  Right.
• 1:31:20
  Yes.
• 1:31:20
  Yes.
• 1:31:21
  Yes.
• 1:31:21
  So future observations, what are you going to do?
• 1:31:23
  James Webb space telescope, obviously try to find even cooler stars and really rule out that there's no stars there.
• 1:31:28
  Yes.
• 1:31:30
  The other one that I thought was really cool.
• 1:31:31
  Interesting.
• 1:31:32
  It was the deep H alpha imaging, right?
• 1:31:34
  This is the idea of really honing in on that hydrogen line, because if the,
• 1:31:40
  there's a bunch of hydrogen there and it's fluorescing, right?
• 1:31:43
  Because it's still interacting with all of the radiation that's in the universe.
• 1:31:49
  Then on the skin of that cloud, you should be able to see H alpha emission that's more prominent than let's say on the interior.
• 1:31:57
  So you should see like a ring around that cloud.
• 1:32:00
  If you really stare at it for long enough, right?
• 1:32:02
  And from that, you could maybe figure out the substructure of that cloud and how that hydrogen is like,
• 1:32:08
  moving around, let's say, or like really clumped together.
• 1:32:11
  Because you would map the outside, and because you then have ideas of how motion and things move in space, then you can sort of project in simulations.
• 1:32:20
  How would you end up with this based on some of the composition?
• 1:32:23
  Exactly, based on all the stuff that's around.
• 1:32:25
  And you can figure out the ionization rate of that hydrogen and the gas density profile.
• 1:32:30
  The other cool thing is there's a new...
• 1:32:34
  Array coming up, the Square Kilometer Array.
• 1:32:36
  Oh, yes.
• 1:32:37
  It's going to be in Australia, and it's going to be in South Africa.
• 1:32:39
  It's set to come online in 2027.
• 1:32:42
  This is an artist's rendition.
• 1:32:44
  They haven't made these yet.
• 1:32:45
  Okay?
• 1:32:45
  But it's going to be massive.
• 1:32:47
  This is very big.
• 1:32:47
  There's going to be a bunch.
• 1:32:48
  It's going to be VLA on steroids.
• 1:32:50
  This is, like, way bigger than VLA.
• 1:32:51
  Yeah, it's going to be the very large array, but very, very, very large array, the square kilometer array.
• 1:32:57
  And it's expected to detect thousands of these relics.
• 1:33:00
  So if we can really hone in on how to find a relic using this one that we found, then we could transform the study of...
• 1:33:09
  these into a statistical science rather than it's a one-off deal this kind of maps conceptually onto the idea of the our previous story where the string theorists created the framework that then was very easily able to be applied to biology in a new category with new data this is same same yeah this is same same not different different yeah but the idea that past work is helping to lower the barrier yeah and that's always something that science does right past work is always good for
• 1:33:33
  The future.
• 1:33:34
  We love to see Compounding Valley.
• 1:33:36
  This was great.
• 1:33:38
  I thought this was very cool.
• 1:33:39
  This was very cool.
• 1:33:40
  And we are going to have our call out for those who have listened to what has been one of our most tightly run pods, especially with our new season two setup.
• 1:33:51
  This was great for episode two.
• 1:33:53
  If you've listened this far and you've stayed, it's helpful for us to know.
• 1:33:57
  So the comment of those for those who stayed is...
• 1:34:01
  what do you think the reason is why this was named Cloud9 or any other Cloud9 related puns?
• 1:34:08
  Yeah, because I don't know why it was named Cloud9.
• 1:34:10
  We don't know.
• 1:34:10
  And actually, one thing I do want to shout out is a friend of the pod, Daniel Gilman.
• 1:34:15
  Yes.
• 1:34:16
  I actually asked him about this story.
• 1:34:17
  Yeah.
• 1:34:18
  And he connected me with Julio, who's one of the...
• 1:34:22
  authors of this paper and we had a really quick email exchange where i had some questions about some of the stuff that was said on this so you know thank you guys for actually indulging and like giving me a little bit of your time because i know you guys are busy trying to figure out what dark matter is and all this stuff but very cool paper julio yes yes and thank you for the work and hopefully if you take a listen to it we did it justice
• 1:34:46
  Yes, let us know.
• 1:34:48
  If not, always send over a correction for us, which brings up our next section, which is going to be corrections from last episode.
• 1:34:57
  Yeah, these aren't that egregious, but, you know, as always, we try to stay true to who we are.
• 1:35:02
  So if we do make mistakes, we do want to shout them out.
• 1:35:05
  Okay, first one.
• 1:35:06
  Not a science mistake, more of a literature mistake.
• 1:35:09
  I said that...
• 1:35:10
  I was quoting Julius Caesar, the famous Shakespeare play, and I said that the friends Romans countrymen lend me your ears...
• 1:35:18
  And also, you know, but Brutus was an honorable man, and they're all honorable men.
• 1:35:22
  I said that was Augustus, a.k.a.
• 1:35:25
  Octavius.
• 1:35:26
  No, that was a speech by Mark Antony at Caesar's funeral.
• 1:35:30
  Yes.
• 1:35:30
  And it's an amazing speech because that's the speech that finally convinces all of Romans that Brutus is actually a bad guy.
• 1:35:37
  And maybe killing Caesar was not a good thing.
• 1:35:39
  Yeah.
• 1:35:41
  The second, this is more of not really a correction.
• 1:35:44
  This is kind of an interesting tidbit.
• 1:35:45
  I was talking to my friend Alan Southworth who works in energy in California, and he was actually telling me that energy markets might be an exception to the square root law because energy markets don't really work the same way that commodities markets or stock markets or crypto markets work because –
• 1:36:05
  The demand is like not elastic at all.
• 1:36:09
  Like the price and demand stuff is not elastic at all.
• 1:36:11
  Because imagine, right?
• 1:36:13
  Let's say that like there's a demand for electricity at a certain rate.
• 1:36:17
  Like we're offering to buy electricity at a certain rate.
• 1:36:22
  At that rate, it might only be feasible for certain electricity producers to meet that demand because it could be wind or solar or things like that because turning on additional wind turbines is not really that hard.
• 1:36:36
  Right, right, right.
• 1:36:37
  On the other hand, turning on like nuclear or turning on natural gas, you've got to buy a bunch of natural gas to turn something on, right?
• 1:36:44
  So –
• 1:36:46
  So it's not so trivial to just be like, oh, you need this much amount of electricity.
• 1:36:50
  Let me just go turn on my gas burner.
• 1:36:54
  So there, that square root law might not actually work.
• 1:36:58
  So it might not be truly universal.
• 1:37:00
  Universal.
• 1:37:01
  And that's something that the Tokyo Stock Exchange might not actually account for, which is what that paper was.
• 1:37:06
  That paper was very specifically for the Tokyo Stock Exchange.
• 1:37:10
  Right.
• 1:37:10
  Perhaps it's different for these other types of markets.
• 1:37:13
  Great, great insight and note from Friend of the Pod.
• 1:37:16
  Yeah.
• 1:37:16
  And then the final one was I did say that there was a Japanese satellite that took that photo of being in between the sun and the earth, taking that photo of the moon coming in between and crashing it.
• 1:37:28
  That's not actually true.
• 1:37:28
  That's from the Deep Space Climate Observatory, which is a NASA satellite.
• 1:37:32
  So we want to give NASA its due credit.
• 1:37:34
  That was NASA, not the Japanese.
• 1:37:36
  As always.
• 1:37:37
  The Americans.
• 1:37:38
  Yeah.
• 1:37:38
  We're doing it.
• 1:37:39
  Whenever something good, really good, or really bad is happening, a reasonable response is the Americans.
• 1:37:47
  Yep.
• 1:37:48
  So those are the three corrections.
• 1:37:49
  Three corrections.
• 1:37:50
  And just as a quick reminder, we love comments.
• 1:37:54
  We love seeing the back and forth and discussion.
• 1:37:56
  Keep commenting.
• 1:37:57
  Keep sharing.
• 1:37:58
  If you work in a lab, bring it into your next lab meeting.
• 1:38:01
  Just play them a snippet.
• 1:38:03
  People will love it.
• 1:38:04
  You're going to sound great and amazing.
• 1:38:06
  so we appreciate the sharing we're trying to fight the billionaire algorithms as best that we can and we're trying to provide some mental health benefits to the people of the world by stretching out some of those neurons getting them exercised your feeds are full of enough random nonsense uh so those who tune in and listen to the full thing we really appreciate you again great episode today we started off
• 1:38:32
  String theory applying to biological networks.
• 1:38:34
  Yep.
• 1:38:35
  That was a nice crossover episode.
• 1:38:36
  It's like when Power Rangers went on whatever thing you learned.
• 1:38:40
  The Power Rangers from space hooked up with the Power Rangers from Mighty Morphin.
• 1:38:44
  Yeah.
• 1:38:45
  Great first story.
• 1:38:46
  We did our rundown.
• 1:38:47
  We had some fascinating tidbits.
• 1:38:50
  Jellyfish sleep, even though they don't have a brain.
• 1:38:52
  Yeah, that's a cool one.
• 1:38:53
  And it's because it damages your DNA.
• 1:38:55
  And we may have found a genuinely new celestial object in Cloud 9.
• 1:39:02
  More soon.
• 1:39:03
  We shall see.
• 1:39:04
  We shall see.
• 1:39:05
  A great spectrum of institutions with papers in Nature Communications, Nature, and the Astrophysical Journals.
• 1:39:13
  I'm your host, Lester Nare, joined as always by my co-host and our resident PhD, who is off-center in this broadcast.
• 1:39:22
  So we'll make sure that he's more set.
• 1:39:24
  I'm sure someone's going to say some shit.
• 1:39:26
  Oh, why are you leaning into the camera?
• 1:39:28
  Can't we get the camera angle centered?
• 1:39:29
  Yeah, you can't do anything right.
• 1:39:32
  We really appreciate you all so much.
• 1:39:34
  As always, this is from First Principles.