Speaker 1: Welcome to Bedtime Astronomy. Explore the wonders of the cosmos
Speaker 1: with our soothing Bedtime astronomi podcast. Each episode offers a
Speaker 1: gentle journey through the stars, planets, and beyond, perfect for
Speaker 1: unwinding after a long day. Let's travel through the mysteries
Speaker 1: of the universe as you drift off into a peaceful
Speaker 1: slumber under the night sky.
Speaker 2: Welcome back today. We are strapping in for quite a journey.
Speaker 2: We're diving into a universe that doesn't actually exist in
Speaker 2: the sky, but inside a supercomputer.
Speaker 3: That's right, we're talking about the Flagship two Galaxy Mocks simulation.
Speaker 3: It's genuinely the largest, most detailed synthetic simulation of the
Speaker 3: Cosmos ever put.
Speaker 2: Together, an unprecedented scientific achievement, really, and it's absolutely foundational
Speaker 2: for what's coming next in astronomy.
Speaker 3: Absolutely, So our mission today is to unpack this thing,
Speaker 3: Flagship two. If you're say, prepping for a big meeting
Speaker 3: on where cosmology is headed, or maybe you just want
Speaker 3: to grasp how scientists are actually going to handle the
Speaker 3: insane amount of data coming.
Speaker 2: Our way, then this is the place to be. This simulation.
Speaker 2: It's not just some cool tech demo.
Speaker 3: No, not at all. It's the essential blueprint, the roadmap
Speaker 3: for discovery.
Speaker 2: And the scale of this blueprint, that's what really jumps
Speaker 2: out first. When we say largest, we mean well, utterly
Speaker 2: staggering numbers. We're talking about a digital universe with three
Speaker 2: point four billion simulated galaxies inside it. Just try to
Speaker 2: wrap your head around that amount of information.
Speaker 3: It's almost impossible. And scientists build this this simulated reality
Speaker 3: very deliberately to get ready for and then you know,
Speaker 3: ultimately interpret the flood of data that's coming from ESA's
Speaker 3: EUCLID mission.
Speaker 2: The European Space Agency's big eye in the sky exactly.
Speaker 3: And what's really critical here, you see, is that this
Speaker 3: simulation does two key jobs. It there's two masters if
Speaker 3: you like. Okay, On one hand, preparation, it lets the
Speaker 3: EUCLID team build and crucially test their analysis methods, their
Speaker 3: software pipelines.
Speaker 2: Right ironing out the bugs before the real data hits precisely.
Speaker 3: But on the other hand, there's a kind of prophecy aspect.
Speaker 3: Because the simulation is built on our current best understanding
Speaker 3: of the universe, the standard model of cosmology, it creates
Speaker 3: this perfect theoretical prediction.
Speaker 2: Ah, So it's like here's what the universe should look
Speaker 2: like according to our theories.
Speaker 3: See exactly, it builds the target that reality. The actual
Speaker 3: data from EUCLID has the other hit or miss.
Speaker 2: And as we dig into the sources talking about this work,
Speaker 2: you know, stuff from the EUCHID Consortium and especially these
Speaker 2: amazing algorithms from USh professor Yokenstatal in his team, we
Speaker 2: start to see the whole point is actually designed to
Speaker 2: find the flaws. They're actively looking for where reality might
Speaker 2: just shatter the theory. They call it searching for cracks
Speaker 2: in the standard model.
Speaker 3: It's a really sophisticated strategy. We're going to explore how
Speaker 3: they built this computational colossus, why it's basically the only
Speaker 3: way forward when you're facing this data avalanche, and maybe
Speaker 3: most excitingly, what kind of potential scientific revolution it could
Speaker 3: actually trigger.
Speaker 2: Okay, let's unpack this journey then, starting with just the
Speaker 2: mind bending scale and the cleverness needed to build this
Speaker 2: digital cosmos in the first place. You really have to
Speaker 2: start with the architect, don't we Behind this massive scale
Speaker 2: flagship two you mentioned Youakim Statle, the astrophysicist at UZH.
Speaker 2: He developed the algorithms that really underpin the whole simulation.
Speaker 3: That's the key, because when you're trying to model three
Speaker 3: point four billion objects, the complexity it just explodes. It's
Speaker 3: not linear, it's exponential.
Speaker 2: So it wasn't just about getting a bigger computer, not
Speaker 2: at all.
Speaker 3: It was about having the mathematical insight, the genius really
Speaker 3: to handle the gravitational interactions between all those objects efficiently.
Speaker 2: The famous n body problem, right, calculating how everything pulls
Speaker 2: on everything else exactly.
Speaker 3: And that's the core challenge in any cosmology simulation. Billions
Speaker 3: or in this case, trillions of particles.
Speaker 2: Wow.
Speaker 3: Previous singulations always had to compromise. Either you simulated a
Speaker 3: big volume with low detail or a small volume with
Speaker 3: high detail.
Speaker 2: You couldn't have both, right.
Speaker 3: Statle's work was about optimizing the calculation, finding clever ways
Speaker 3: to group particles and calculate forces so they could manage
Speaker 3: both scale and resolution. The raw computing power is kind
Speaker 3: of useless without the smart algorithms to distribute the work
Speaker 3: and calculate those dynamic interactions across huge cosmic distances effectively.
Speaker 2: And the result of that optimization is just wow. Let's
Speaker 2: say that number again, three point four billion galaxies. That
Speaker 2: gives you the size of the simulated patch of universey
Speaker 2: and the volume. But you pointed out the real complexity
Speaker 2: comes from the depths of the data for each one.
Speaker 2: What kind of details are we talking about for each galaxy?
Speaker 3: Oh, it's incredibly detailed. We're walking high resolution data design
Speaker 3: to mimic what the real EUCLID telescope will actually capture.
Speaker 3: Each of those three point four billion galaxies isn't just
Speaker 3: a point of light. Each one has four hundred modeled properties, four.
Speaker 2: Hundred four hundred properties.
Speaker 3: Per gal like what, well, the basics you'd expect brightness,
Speaker 3: it's position in three D space, it's velocity, how fast
Speaker 3: it's moving. But then crucial details like its metallicity, how
Speaker 3: many heavy elements it has. Okay, it's star formation history,
Speaker 3: when its stars were born, and critically for one of
Speaker 3: euclid's main goals, it's precise shape and orientation. It's ellipticity.
Speaker 2: Why is the shape the ellipticity so important that they'd
Speaker 2: spend all that computational effort modeling at three point four billion.
Speaker 3: Times ah because that's the bedrock of the dark matter
Speaker 3: mapping effort. We'll get more into this, but basically, EUCLID
Speaker 3: finds dark matter by looking for tiny distortions in the
Speaker 3: shapes of background galaxies caused by gravitational.
Speaker 2: Lenses right the like it's bent by unseen mass exactly.
Speaker 3: So, if your synthetic universe, your simulation doesn't have accurately
Speaker 3: modeled galaxy shapes to begin with, you can't properly train
Speaker 3: the software that's supposed to detect those lensing distortions in
Speaker 3: the real data, So.
Speaker 2: You wouldn't know if your software was working correctly.
Speaker 3: Precisely, you couldn't accurately and interpret the real observations. Those
Speaker 3: four hundred properties are what tie the simulation directly to
Speaker 3: the specific physics and the specific measurements EUCLID is designed to.
Speaker 2: Make okay and tracing back even further before they even
Speaker 2: put the galaxies in the underlying structure, the cosmic web,
Speaker 2: that came from an even bigger calculation, didn't it.
Speaker 3: Oh yes, before populating it with galaxies, the simulation first
Speaker 3: built the large scale structure, the scaffolding of the universe,
Speaker 3: you could say, the dark matter halos and the filaments.
Speaker 2: Connecting them, the cosmic web.
Speaker 3: That's it, and that structure was built by tracking the
Speaker 3: gravitational interactions. Wait for it, four trillion particles, four trillion trillon,
Speaker 3: four trillion individual points, each representing a chunk of mass
Speaker 3: pulling on every other chunk over billions of years of
Speaker 3: simulated cosmic time.
Speaker 2: That number just defines the sheer massive scope of that
Speaker 2: initial n body calculation. You're modeling every significant lump and
Speaker 2: bump in matter density across this huge volume of space,
Speaker 2: simulating gravity's effect over cosmic history.
Speaker 3: Think about the memory needed just to store the position
Speaker 3: and velocity of four trillion points at any given moment, let.
Speaker 2: Alone calculate all the forces between them constantly exactly.
Speaker 3: It's a staggering computational.
Speaker 2: Feat and managing that four trillion interacting particles in one
Speaker 2: giant calculation that's almost beyond imagining. It needed serious computing power,
Speaker 2: right institutional scale. Where did they actually do this?
Speaker 3: Yeah, this monster calculation was run back in twenty nineteen.
Speaker 3: They used the piz Dint supercomputer at the Swiss National
Speaker 3: Supercomputing Center CSCs and Lugano, Switzerland.
Speaker 2: PiZZ Date wasn't that one of the fastest in the
Speaker 2: world at the time It was.
Speaker 3: Indeed, in twenty nineteen, PiZZ Date was ranked the third
Speaker 3: most powerful supercomputer on the planet.
Speaker 2: Okay, and to give people context on what that means
Speaker 2: in terms of resources, the sources really highlight the commitment involved.
Speaker 2: This wasn't just like a big job they ran overnight.
Speaker 3: No far from it. Get this, more than eighty percent
Speaker 3: of PiZZ Dant's entire capacity was dedicated solely to this
Speaker 3: one calculation.
Speaker 2: Eighty percent. Imagine basically shutting down almost everything else happening
Speaker 2: at a major national supercomputing center just to run one simulation.
Speaker 3: It shows you how important this was and just how
Speaker 3: challenging it was computationally. Statle himself said, and I'm quoting here,
Speaker 3: it was a huge challenge to simulate such a large
Speaker 3: portion of the universe at this resolution in a single calculation.
Speaker 2: Because it had never been done before. They needed that massive,
Speaker 2: sustained dedication of resources.
Speaker 3: Absolutely, it pushed the limits.
Speaker 2: So it's important for people listening to understand this two
Speaker 2: step process. Right First, the pure physics track, those four
Speaker 2: trillion particles, let gravity shape the cosmic web over billions
Speaker 2: of years. That builds the underlying framework.
Speaker 3: Yes, the scaffolding, the dark matter distribution. That's step one.
Speaker 3: Step two is then more astrophysical and arguably more complex
Speaker 3: in some ways. Once you have those structures, the dark
Speaker 3: matter halos where galaxy should form the connecting filaments, you
Speaker 3: then have to populate them with synthetic galaxies. And these
Speaker 3: aren't just random points. They have to obey the known
Speaker 3: physical laws of how galaxies actually form and evolve, and critically,
Speaker 3: they have to mimic exactly what the EUCLID instruments will observe.
Speaker 2: So they look like real galaxies to the telescopes, cameras
Speaker 2: and spectrographs.
Speaker 3: Exactly complete with those four hundred properties for each of
Speaker 3: the three point four billion galaxies. This is what produces
Speaker 3: that realistic blueprint Statle talks about a simulation of what
Speaker 3: yuclind will actually see when it looks to the sky.
Speaker 2: So the gravitational physics creates the container, the large scale structure,
Speaker 2: and then the astrophysics fills that container with the right
Speaker 2: kinds of objects, the things EUCLID is actually looking for,
Speaker 2: the light sources. That interplay between modeling the invisible dark
Speaker 2: matter structures and the visible galaxies is really the genius
Speaker 2: of Flagship two.
Speaker 3: It bridges the gap between fundamental theory and actual observation,
Speaker 3: which brings us neatly to the crucial why why go
Speaker 3: to all this trouble? Why dedicate eighty percent of one
Speaker 3: of the world's fastest supercomputers to building a fake universe?
Speaker 2: And the answer really comes down to the sheer amount
Speaker 2: of data that the real EUCLID mission is generating.
Speaker 3: Exactly, let's quickly ground ourselves in what EUCLID is doing.
Speaker 3: It's an ESA space telescope launched in June twenty twenty
Speaker 3: three started its survey, and it's designed to look at
Speaker 3: a huge chunk of the sky over a third of
Speaker 3: the entire celestial sphere, with unprecedented resolution both in imaging
Speaker 3: and spectroscopy.
Speaker 2: That combination of vast area and sharp detail that's the
Speaker 2: source of the problem, isn't it the data logistics problem?
Speaker 3: It is. Julian Adamik, one of the key collaborators on
Speaker 3: Flagship two, puts it very clearly. He explains that EUCLID
Speaker 3: produces data in such sheer volume and speed that having
Speaker 3: humans analyze it manually is just completely impossible.
Speaker 2: Give us a sense of that impossibility. You mentioned three
Speaker 2: point four billion galaxies in the simulation right now.
Speaker 3: Imagine trying to process the real data for billions of
Speaker 3: galaxies and they have Hypothetically, a highly trained astrophysicist could
Speaker 3: analyze everything about one galaxy in just one.
Speaker 2: Second, which is ridiculously fast, totally unrealistic.
Speaker 3: Yeah, but even then it would take centuries of NonStop
Speaker 3: work just to get through the catalog once. And EUCLID
Speaker 3: is delivering this data continuously day after day.
Speaker 2: Wow, that's yeah, that's a staggering bottleneck. It almost feels
Speaker 2: a bit demoralizing. Scientists build this incredible instrument that produces
Speaker 2: data they know they can't possibly look at themselves.
Speaker 3: Well, it's just the focus. The human element moves away
Speaker 3: from clicking on individual galaxies towards designing the algorithms the
Speaker 3: automated systems that can handle the load. And this is
Speaker 3: why the mock data, the Flagship two simulation is absolutely crucial.
Speaker 2: Because you need something to test those algorithms on before
Speaker 2: the real, precious unique data arived.
Speaker 3: Exactly, you have to develop and rigorously test the methodology
Speaker 3: for interpreting these massive, complex data sets in advance. You
Speaker 3: get afford to wait for the real data stream to
Speaker 3: start and then begin figuring out how to process.
Speaker 2: We just fall hopelessly behind.
Speaker 3: Hopelessly you need to be ready to process it almost instantly,
Speaker 3: often within hours or days of it coming down from
Speaker 3: the telescope because the data pipelines have to run continuously
Speaker 3: to keep up.
Speaker 2: So the simulation Flagship two becomes the ultimate training ground.
Speaker 2: It's the perfect controlled environment for the AI and machine
Speaker 2: learning algorithms that have to do the heavy lifting.
Speaker 3: That's a great way to put it. If the simulation
Speaker 3: is this perfect theoretical universe based on known physics, scientists
Speaker 3: can feed that simulated data into their algorithms.
Speaker 2: And see if the algorithms correctly identify all three point
Speaker 2: four billion fake galaxies and measure their four hundred properties accurately.
Speaker 3: Yes, And just as importantly, see if the algorithms correctly
Speaker 3: identify and handle the errors and complexities they'll encounter in real.
Speaker 2: Data, like what kind of errors.
Speaker 3: Things like instrumental effects, noise cosmic rays hitting the detector,
Speaker 3: but also tricky astrophysical things like accurate measuring the shapes
Speaker 3: for lensing, or dealing with deeplending objects. Yeah, where two
Speaker 3: or more galaxies overlap in the image on the sky
Speaker 3: and the software has to figure out that they're separate
Speaker 3: objects and measure their properties individually.
Speaker 2: That's really hard, I can imagine. So if your mock
Speaker 2: data Flagship two is this perfect high resolution replica where
Speaker 2: you know the ground truth, you know exactly where every
Speaker 2: galaxy is and what its property should be, you know
Speaker 2: the right then you can run your software on it,
Speaker 2: see where it makes mistakes, and fine tune it until
Speaker 2: it reaches the accuracy and reliability needed for the real mission.
Speaker 3: Exactly, you calibrate your tools on the simulator before you
Speaker 3: use them on the real sky.
Speaker 2: And this also helps tackle that huge problem in astronomy
Speaker 2: systematic errors. When you look at the real universe and
Speaker 2: find something weird, how do you know if it's genuinely
Speaker 2: new physics or just some subtle bias in your telescope
Speaker 2: or your analysis software.
Speaker 3: Flagship two helps isolate that if your algorithm process is
Speaker 3: the perfect synthetic universe and consistently finds, say that it
Speaker 3: underestimates the distances to small, faint galaxies, then you know
Speaker 3: that bias comes from the algorithm itself or how it
Speaker 3: interacts with simulated instrument effects, not from some strange new
Speaker 3: property of the actual universe. You can then fix the
Speaker 3: code before you let it loose on the real euclid data.
Speaker 2: It really underscores how modern astronomy isn't just about pointing
Speaker 2: telescopes anymore. It's deeply intertwined with sophisticated data science, computer science,
Speaker 2: preemptive coding.
Speaker 3: Absolutely, the modern astronomer, especially on these big survey projects,
Speaker 3: is often as much a data pipeline manager and algorithm
Speaker 3: developer as they are a traditional observer. That simulation isn't illuxiery,
Speaker 3: it's a necessity. It's like a massive digital dress rehearsal,
Speaker 3: or maybe a digital safety net.
Speaker 2: Okay, now we get to what, for many people is
Speaker 2: the really exciting part, the potential scientific drama, because while
Speaker 2: Flagship two is fundamentally this incredible technical exercise in preparation.
Speaker 3: Data handling, algorithm testing, its.
Speaker 2: Ultimate scientific purpose is actually to challenge our current understanding
Speaker 2: of the universe, to actively seek out the flaws in
Speaker 2: our best theories.
Speaker 3: It really is a high stakes test, and it's essential
Speaker 3: for listeners to remember, as we discussed that Flagship two,
Speaker 3: the mock universe is built entirely on the foundation of
Speaker 3: the standard cosmological model, which.
Speaker 2: Is called Lambda CDM. Right, can we just quickly recap
Speaker 2: what that model actually assumes?
Speaker 3: Sure, land to CDM basically says the universe is made
Speaker 3: up of a few key ingredients governed by Einstein's general relativity.
Speaker 3: There's ordinary matter like us, stars, gas, but much more importantly,
Speaker 3: there's cold dark matter.
Speaker 2: CDM, invisible slow moving.
Speaker 3: Stuff exactly invisible, doesn't interact with light, moves relatively slowly,
Speaker 3: and its gravity dictates where structures like galaxies and galaxy
Speaker 3: clusters form. That's the CDM part, okay, and the lambda
Speaker 3: landis represents the cosmological constant. This is the simplest mathematical
Speaker 3: form of dark energy, an intrinsic energy of space itself
Speaker 3: that causes the expansion of the universe to accelerate.
Speaker 2: So dark mata pulls things together, dark energy pushes things apart,
Speaker 2: basically in simple terms.
Speaker 3: Yes, and this LAMB to CDM model, despite its weird ingredients,
Speaker 3: has been incredibly successful. It explains a vast range of
Speaker 3: observations from the cosmic microwave background radiation left over from
Speaker 3: the Big Bang right up to the large scale distribution
Speaker 3: of galaxies we see today.
Speaker 2: So Flagship two simulates a perfect LAMB to CDM universe,
Speaker 2: and the expectation as both statal and atomic mention is
Speaker 2: that euclid's actual observations will broadly speak and confirm the
Speaker 2: matter distribution predicted by Flagship two. That's the baseline, hope
Speaker 2: or expectation.
Speaker 3: That's the baseline. But science rarely progresses just by confirming
Speaker 3: what we already think we know. The real excitement lies
Speaker 3: in the potential for disagreement. Scientists are actively hoping to
Speaker 3: find places where the model breaks down.
Speaker 2: They explicitly anticipate surprises and on a expected discoveries. As
Speaker 2: the sources say, they seem quite convinced the model isn't
Speaker 2: the final word.
Speaker 3: Well, yes, because there are already hints tensions in existing data.
Speaker 3: Statle uses that striking phrase, we already see indications of
Speaker 3: cracks in the standard model.
Speaker 2: That sounds significant. What sort of cracks is he likely
Speaker 2: referring to? Where are these tensions showing up already?
Speaker 3: There are a few areas. Probably the most famous is
Speaker 3: the Hubble tension. Different methods of measuring the current expansion
Speaker 3: rate of the universe the Hubble constant are giving slightly
Speaker 3: but persistently different answers.
Speaker 2: Measuring it locally using supernovae gives one value. Measuring it
Speaker 2: based on the early universe light gives another.
Speaker 3: Exactly and they don't quite agree within their error bars.
Speaker 3: That's a crack.
Speaker 2: Yeah.
Speaker 3: There are also potential issues, though perhaps less statistically strong,
Speaker 3: with how dark matter seems to clump on smaller scales
Speaker 3: compared to LAMB to CDM predictions, or maybe slight inconsistencies
Speaker 3: in gravitational lensing measurements.
Speaker 2: So these are subtle discrepancies found by cobbling together data
Speaker 2: from different telescopes different methods.
Speaker 3: Right, And the hope or expectation is that EUCLID, with
Speaker 3: its huge uniform high precision survey covering billions of years
Speaker 3: of cosmic time in one.
Speaker 2: Go, might finally provide data clear enough and statistically powerful
Speaker 2: enough to show definitively whether these cracks are real and
Speaker 2: maybe reveal new phenomena that LAMB to CDM just cannot explain,
Speaker 2: things about the cosmic web structure or galaxy evolution that
Speaker 2: don't fit the simple.
Speaker 3: Rules that tension really defines the scientific knife edge. Here
Speaker 3: Atomic sums it up perfectly. It will be exciting to
Speaker 3: see whether the model holds up against euclid's high precision data,
Speaker 3: or whether we uncover signs of new shortcomings.
Speaker 2: So Flagship two acts as the perfect theoretical benchmark. It's
Speaker 2: the idealized prediction. EUCLID delivers the messy, complicated.
Speaker 3: Reality, and of that reality, the EUCLID data deviates significantly
Speaker 3: and systematically from the flagship two prediction.
Speaker 2: You can't just blame the simulation because you know exactly
Speaker 2: what physics went into it standard Lambda CDM precisely.
Speaker 3: The blame has to fall on the input physics. The
Speaker 3: assumptions of land to CDM must be wrong or at
Speaker 3: least incomplete.
Speaker 2: And that deviation that confirmed crack. That's where the scientific
Speaker 2: revolution would have to start.
Speaker 3: Absolutely. It would force cosmologists back to the drawing board.
Speaker 3: Do we need more complex form of dark energy? Does
Speaker 3: dark matter have some unexpected properties? Does gravity itself behave
Speaker 3: differently on cosmic scales than Einstein predicted?
Speaker 2: Maybe considering alternative theories like mond, modified Newtonian dynamics or
Speaker 2: other modified gravity ideas.
Speaker 3: Potentially Yes, the simulation, by providing that precise baseline, clarifies
Speaker 3: exactly where and by how much the standard model is failing.
Speaker 3: It points the way for the next generation of theories.
Speaker 2: Okay, let's zoom in on one of those potentially revolutionary areas,
Speaker 2: dark energy it's arguably the best mystery in cosmology. And
Speaker 2: within that standard LAMB to CDM model that Flagship two
Speaker 2: is built on, dark energy is represented in the simplest
Speaker 2: possible way.
Speaker 3: Right exactly as Statle puts it quite bluntly. In the model,
Speaker 3: dark energy is just a constant. It's the cosmological constant Lambda,
Speaker 3: meaning it's unchanging, unchanging in space, unchanging in time. It's
Speaker 3: treated as an intrinsic property of space itself, a constant
Speaker 3: energy density everywhere that doesn't dilute as the universe expands.
Speaker 3: It's the simplest mathematical term you can add to Einstein's
Speaker 3: equations to get accelerated expansion.
Speaker 2: But the whole point of EUCLID, one of its primary
Speaker 2: missions is to rigorously test whether that assumption holds true.
Speaker 2: Is it really just a constant? Why is finding that
Speaker 2: out so critical?
Speaker 3: Because if it's not a constant, if its strength or
Speaker 3: density has changed over cosmic history, then it's not just
Speaker 3: some background property of space time. It must be something dynamic,
Speaker 3: something physical that evolves.
Speaker 2: Which would completely change our picture of the universe's past
Speaker 2: and presumably its future fate.
Speaker 3: Absolutely, if it's a true constant Lambda, the acceleration continues relentlessly,
Speaker 3: leading eventually to a big freeze or maybe even a
Speaker 3: big rip where everything gets torn apart. But if dark
Speaker 3: energy is dynamic, if its strength changes, well, all bets
Speaker 3: are off. It could fade away, could strengthen, It could oscillate.
Speaker 2: And isn't this where theoretical ideas like quintessence come in?
Speaker 3: Yes, exactly. Quintessence is the general name physicists give to
Speaker 3: hypothetical dynamic forms of dark energy, usually pictured to some
Speaker 3: kind of slowly evolving scalar field spread throughout space, a
Speaker 3: bit like the field that might have driven inflation in
Speaker 3: the very early universe, but operating now.
Speaker 2: So finding evidence that lambda isn't constant would be huge.
Speaker 2: It would mean moving from this simple placeholder to needing
Speaker 2: a whole new physical theory of this dynamic field.
Speaker 3: Precisely, it opens up a whole new realm of fundamental physics.
Speaker 2: So how does EUCLID actually test this? How can it
Speaker 2: tell if dark energy was the same strength billions of
Speaker 2: years ago as it is today.
Speaker 3: It comes back to the sheer scale and depth of
Speaker 3: its survey. EUCLID is mapping the positions and distances of
Speaker 3: billions of galaxies across a huge volume of space looking
Speaker 3: back up to ten billion years in cosmic time.
Speaker 2: So it's effectively creating snapshots of the universe at different ages.
Speaker 3: That's a good way to think of it. By measuring
Speaker 3: galaxies at different look back times, astronomers can reconstruct the
Speaker 3: history of cosmic expansion.
Speaker 2: How do they measure the expansion rate at those past times?
Speaker 2: Is it just about how clustered the galaxies are.
Speaker 3: It's primarily done through measuring distances and redshifts very precisely. Redshift,
Speaker 3: as you know, is the stretching of light's wavelength as
Speaker 3: it travels through the expanding universe.
Speaker 2: The farther away something is, the more its light is stretched,
Speaker 2: the higher it's red shift.
Speaker 3: Correct, and the relationship between distance and redshift isn't constant.
Speaker 3: It depends on how fast the universe was expanding at
Speaker 3: different times in the past. Euclid uses techniques like measuring
Speaker 3: baryon acoustic oscillations characteristic patterns in how galaxies cluster as
Speaker 3: a sort of standard ruler to get precise distances at
Speaker 3: different redshifts.
Speaker 2: Ah okay, So by measuring these standard rulers at various
Speaker 2: distances and thus various past times. They can chart the
Speaker 2: expansion history.
Speaker 3: Precisely ademic frames the core question they're asking. We can
Speaker 3: see how the universe expanded at that time, looking back
Speaker 3: billions of years, and measure whether this constant really remained constant.
Speaker 3: They're literally mapping the acceleration rate over cosmic time.
Speaker 2: And if that acceleration rate was different, say five billion
Speaker 2: years ago compared to ten billion years ago, or compared
Speaker 2: to it today.
Speaker 3: Then LAMB is not a constant. Game over for the
Speaker 3: simplest model, that would be the smoking gun evidence that
Speaker 3: we need new physics beyond standard LAMB to CDM to
Speaker 3: explain dark energy.
Speaker 2: Now, Statle does add a note of caution rate. He
Speaker 2: suggests you could might not deliver the final definitive answer
Speaker 2: on dark energy overnight.
Speaker 3: Yeah, he manages expectations a bit. It's unlikely to be
Speaker 3: a single Eureka moment from one data release. Understanding dark
Speaker 3: energy is incredibly challenging, but he expresses strong confidence that
Speaker 3: you could will bring us a step closer to understanding
Speaker 3: the mysterious realm of dark energy.
Speaker 2: It's about gathering that unprecedentedly precise day data and needed
Speaker 2: to really start ruling out possibilities. Can we rule out
Speaker 2: the simple constant. If so, what kind of dynamic behavior
Speaker 2: does the data favor exactly?
Speaker 3: And again, having flagship two, which was built assuming lambda
Speaker 3: as a constant, provides that perfect reference point. Any deviation
Speaker 3: UCLI finds in the actual expansion history will stand out
Speaker 3: starkly when compared to the simulation's prediction. It sharpens the search.
Speaker 2: So to achieve these really ambitious goals testing the constancy
Speaker 2: of dark energy, mapping dark matter, looking for cracks in
Speaker 2: the standard model, EUCLID relies on some pretty sophisticated observational techniques.
Speaker 2: We should probably touch on those. It's not just taking
Speaker 2: pretty pictures.
Speaker 3: Definitely not. It's about precision and scale combined. It really
Speaker 3: is the most comprehensive survey ever attempted in terms of
Speaker 3: both the volume covered and the detail captured within that volume.
Speaker 2: Okay, let's start with mapping the invisible stuff dark matter.
Speaker 2: You mentioned gravitational lensing earlier. How does EUCLID actually use that?
Speaker 3: Right? So, EUCLID has incredibly high spatial resolution, meaning it
Speaker 3: can take really sharp images. This allows its researchers to
Speaker 3: detect very subtle distortions in the shapes of literally billions
Speaker 3: of distant background galaxies.
Speaker 2: These aren't the dramatic arcs and multiple images you see
Speaker 2: from strong lensing around massive galaxy cluster.
Speaker 3: No, this is weak lensing. The effect is tiny, just
Speaker 3: a slight statistical preference for galaxies behind a massive structure
Speaker 3: to appear slightly stretched or sheared in a particular direction.
Speaker 2: Like looking through a very subtly warped window pane.
Speaker 3: As you say, exactly, and the warping the distortion is
Speaker 3: caused by the gravitational pull of all the mass lying
Speaker 3: between us and those background galaxies, primarily the invisible clumps
Speaker 3: and filaments of dark matter that make up the cosmic web.
Speaker 2: So by measuring these tiny systematic distortions across billions of
Speaker 2: galaxies spread over the sky.
Speaker 3: Scientists can essentially reverse engineer a map of where the
Speaker 3: intervening mass must be. They can figure out the distribution
Speaker 3: of the stuff doing the bending, even.
Speaker 2: Though they can't see the dark matter directly.
Speaker 3: Correct The result is a massive three dimensional map of
Speaker 3: the dark matter distribution across a huge portion of the universe.
Speaker 3: This map is crucial for testing the predictions of Flagship two,
Speaker 3: which remember simulated precisely how dark matters should be distributed
Speaker 3: according to lambda CDM physics.
Speaker 2: Okay, so weak lensing gives you the map of the
Speaker 2: mass distribution, but you need to know where things are
Speaker 2: in three D space, especially their distances, to make sense
Speaker 2: of cosmic evolution and expansion history. How does EUCLID get
Speaker 2: the distances.
Speaker 3: That's where the complementary technique comes in spectroscopy. EUCLID has
Speaker 3: an instrument called NISP near ineferred spectrometer and photometer that
Speaker 3: can measure the spectrum of light from millions upon millions.
Speaker 2: Of galaxies, breaking the light down into its constituent colors
Speaker 2: like a prism does.
Speaker 3: Exactly, and by doing that, scientists can measure the galaxy's
Speaker 3: redshift with very high accuracy. They look for characteristic features
Speaker 3: in the spectrum absorption or emission lines from specific chemical
Speaker 3: elements and see how much they've been shifted towards words, longer,
Speaker 3: redder wavelengths due to cosmic expansion.
Speaker 2: And as we establish, redshift is the key indicator of
Speaker 2: distance in an expanding universe. More redshift means farther away
Speaker 2: and further back in.
Speaker 3: Time, precisely so, by getting these accurate spectroscopic redshifts for
Speaker 3: a huge number of galaxies euclib can pinpoint their distances
Speaker 3: with unprecedented accuracy. This is what allows them to slice
Speaker 3: the universe up into those different timebins we talked about
Speaker 3: to map the expansion rate over ten billion years.
Speaker 2: So it's the combination the two techniques that's really powerful.
Speaker 2: The sharp imaging gives you the galaxy shapes for weak
Speaker 2: lensing mapping mass, and.
Speaker 3: The sectrocity gives you the precise red shifts for distances
Speaker 3: mapping expansion history and three D position.
Speaker 2: That's the synergy. You put them together and you build
Speaker 2: up this incredibly detailed three dimensional map of both the
Speaker 2: visible galaxies and the invisible dark matter structures spanning this
Speaker 2: enormous cosmic sphere with a radius of ten billion light years,
Speaker 2: a survey of cosmic structure and evolution covering more than
Speaker 2: two thirds of the universe's entire history.
Speaker 3: Just staggering. And there's one more benefit of casting such
Speaker 3: a wide net, isn't there The possibility of finding things
Speaker 3: you aren't even looking for.
Speaker 1: Ah.
Speaker 2: Yes, the element of serendipity. Because euclid is surveying such
Speaker 2: a truly vast volume of space, the chances of stumbling
Speaker 2: upon extremely rare or unexpected objects or phenomena are actually
Speaker 2: quite high.
Speaker 3: A Domic mentions this right, that some things are just
Speaker 3: intrinsically extremely uncommon. Maybe a specific type of superluminous supernova,
Speaker 3: or a weird kind of quasar, or a particular stage
Speaker 3: of galaxy merging that only happens fleetingly.
Speaker 2: Exactly in any small patch of sky. You might never
Speaker 2: see one. But when your survey volume encompasses billions of
Speaker 2: light years, you're bound to catch some of these rare
Speaker 2: events just by sheer statistical luck.
Speaker 3: That's the idea. Adamic says. The chances of finding unexpected
Speaker 3: or rare objects are high. This is pure discovery space.
Speaker 3: You might find objects or phenomena that challenge existing theories
Speaker 3: in ways nobody even anticipated, simply because nobody had looked
Speaker 3: wide enough and deep enough before. Hashtag tag tag outro
Speaker 3: so to kind of wrap this all up. This incredible
Speaker 3: Flagship two simulation, this universe built inside PiZZ dain. It's
Speaker 3: playing these three really critical intertwined roles right now at
Speaker 3: this pivotal moment in astronomy.
Speaker 2: Okay, let's recap them first.
Speaker 3: First, it's the essential data preparation engine. It allowed scientists
Speaker 3: to develop and test the automated software pipelines needed to
Speaker 3: actually handle euclid's massive data volume. Without it, they'd be drowning.
Speaker 2: Got it. Second.
Speaker 3: Second, it's the highest precision test bed ever created for
Speaker 3: the standard LANDB to CDM model of cosmology. It's the
Speaker 3: perfect theoretical prediction against which the real universe will be.
Speaker 2: Measured looking for those cracks.
Speaker 3: And third and third, building on that, it serves as
Speaker 3: the indispensable guide for interpreting the complex cosmic history that
Speaker 3: EUCLID is now observing. By comparing the real data to
Speaker 3: the simulation, scientists can better understand the growth of structure,
Speaker 3: thetion of galaxies, and the nature of dark energy over time.
Speaker 2: And this isn't just theoretical anymore. Things are happening. You
Speaker 2: mentioned the first data release, the quick data release that
Speaker 2: came out in March twenty twenty five.
Speaker 3: That's right. Even that was just a small taste relatively
Speaker 3: speaking of a full survey, but it already provided new insights,
Speaker 3: particularly into galaxy clusters and the structure of the nearby
Speaker 3: cosmic web, and papers based on that and related simulation
Speaker 3: work are already being published in journals like Astronomy and Astrophysics.
Speaker 2: And there's a plan for more data coming soon.
Speaker 3: Yes, the next major planned data release, which will be
Speaker 3: significantly larger, is expected sometime in spring twenty twenty six.
Speaker 3: So the pace is really picking up.
Speaker 2: It's a rapid unfolding of discovery where every step relies
Speaker 2: heavily on that groundwork laid by the simulation. Those processing
Speaker 2: pipelines tested on Flagship two, they're running now on the
Speaker 2: real data.
Speaker 3: Absolutely, the preparation is paying off.
Speaker 2: What really stands out to me thinking about the whole
Speaker 2: project is the almost philosophical intent behind Flagship two. Here
Speaker 2: we have the most sophisticated, detailed digital model of reality
Speaker 2: ever created, built explicitly not just to confirm what we
Speaker 2: think we know, but specifically to find the errors in
Speaker 2: our understanding. It's like building a perfect machine designed to
Speaker 2: highlight its own potential flaws when compared to the real thing.
Speaker 3: That's a great way to put it. It's a theoretical
Speaker 3: construct designed in a sense to be proven wrong or
Speaker 3: at least incomplete, in the most informative way possible.
Speaker 2: And that sets up this fascinating tension that you listening
Speaker 2: should really keep in mind. As the euclid results continue
Speaker 2: to roll in over the next few years exactly.
Speaker 3: You've got this perfect, pristine, synthetic universe inside Flagship two,
Speaker 3: totally controlled high resolution built strictly on the rules of
Speaker 3: LAMB to CDM physics. And then you have the messy,
Speaker 3: noisy complex real data arriving from the EUCLID telescope, full
Speaker 3: of unexpected glitches, statistical fluctuations, and potentially truly baffling outliers.
Speaker 2: So the big question becomes if the broad strokes match.
Speaker 2: If the for all map of the cosmos looks roughly
Speaker 2: like the simulation predicts, but there are these nagging discrepancies,
Speaker 2: maybe dark matter clumps slightly differently in nearby voice and expected,
Speaker 2: or maybe a few of those super rare unpredicted objects
Speaker 2: keep popping up. Which do you trust more?
Speaker 3: Do you emphasize the broad confirmation that seems to reinforce
Speaker 3: decades of established physics, or do you focus on those
Speaker 3: unexpected outliers, the cracks, the things that don't fit the
Speaker 3: model and seem to demand entirely new.
Speaker 2: Theories that tension right there between the expected pattern and
Speaker 2: the surprising detail. That's where the future of cosmology is
Speaker 2: being forged. That's where the discoveries will likely lie.
Speaker 3: Absolutely it's an exciting time to be watching. Thanks for
Speaker 3: joining us for this exploration.
Speaker 2: We'll see you next time.
Speaker 3: The school
Speaker 2: Last