Mapping the Cosmos: Euclid’s Flagship Simulation

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Welcome back today. We are strapping in for quite a journey. We're diving into a universe that doesn't actually exist in the sky, but inside a supercomputer.

That's right, we're talking about the Flagship two Galaxy Mocks simulation. It's genuinely the largest, most detailed synthetic simulation of the Cosmos ever put.

Together, an unprecedented scientific achievement, really, and it's absolutely foundational for what's coming next in astronomy.

Absolutely, So our mission today is to unpack this thing, Flagship two. If you're say, prepping for a big meeting on where cosmology is headed, or maybe you just want to grasp how scientists are actually going to handle the insane amount of data coming.

Our way, then this is the place to be. This simulation. It's not just some cool tech demo.

No, not at all. It's the essential blueprint, the roadmap for discovery.

And the scale of this blueprint, that's what really jumps out first. When we say largest, we mean well, utterly staggering numbers. We're talking about a digital universe with three point four billion simulated galaxies inside it. Just try to wrap your head around that amount of information.

It's almost impossible. And scientists build this this simulated reality very deliberately to get ready for and then you know, ultimately interpret the flood of data that's coming from ESA's EUCLID mission.

The European Space Agency's big eye in the sky exactly.

And what's really critical here, you see, is that this simulation does two key jobs. It there's two masters if you like. Okay, On one hand, preparation, it lets the EUCLID team build and crucially test their analysis methods, their software pipelines.

Right ironing out the bugs before the real data hits precisely.

But on the other hand, there's a kind of prophecy aspect. Because the simulation is built on our current best understanding of the universe, the standard model of cosmology, it creates this perfect theoretical prediction.

Ah, So it's like here's what the universe should look like according to our theories.

See exactly, it builds the target that reality. The actual data from EUCLID has the other hit or miss.

And as we dig into the sources talking about this work, you know, stuff from the EUCHID Consortium and especially these amazing algorithms from USh professor Yokenstatal in his team, we start to see the whole point is actually designed to find the flaws. They're actively looking for where reality might just shatter the theory. They call it searching for cracks in the standard model.

It's a really sophisticated strategy. We're going to explore how they built this computational colossus, why it's basically the only way forward when you're facing this data avalanche, and maybe most excitingly, what kind of potential scientific revolution it could actually trigger.

Okay, let's unpack this journey then, starting with just the mind bending scale and the cleverness needed to build this digital cosmos in the first place. You really have to start with the architect, don't we Behind this massive scale flagship two you mentioned Youakim Statle, the astrophysicist at UZH. He developed the algorithms that really underpin the whole simulation.

That's the key, because when you're trying to model three point four billion objects, the complexity it just explodes. It's not linear, it's exponential.

So it wasn't just about getting a bigger computer, not at all.

It was about having the mathematical insight, the genius really to handle the gravitational interactions between all those objects efficiently.

The famous n body problem, right, calculating how everything pulls on everything else exactly.

And that's the core challenge in any cosmology simulation. Billions or in this case, trillions of particles.

Wow.

Previous singulations always had to compromise. Either you simulated a big volume with low detail or a small volume with high detail.

You couldn't have both, right.

Statle's work was about optimizing the calculation, finding clever ways to group particles and calculate forces so they could manage both scale and resolution. The raw computing power is kind of useless without the smart algorithms to distribute the work and calculate those dynamic interactions across huge cosmic distances effectively.

And the result of that optimization is just wow. Let's say that number again, three point four billion galaxies. That gives you the size of the simulated patch of universey and the volume. But you pointed out the real complexity comes from the depths of the data for each one. What kind of details are we talking about for each galaxy?

Oh, it's incredibly detailed. We're walking high resolution data design to mimic what the real EUCLID telescope will actually capture. Each of those three point four billion galaxies isn't just a point of light. Each one has four hundred modeled properties, four.

Hundred four hundred properties.

Per gal like what, well, the basics you'd expect brightness, it's position in three D space, it's velocity, how fast it's moving. But then crucial details like its metallicity, how many heavy elements it has. Okay, it's star formation history, when its stars were born, and critically for one of euclid's main goals, it's precise shape and orientation. It's ellipticity.

Why is the shape the ellipticity so important that they'd spend all that computational effort modeling at three point four billion.

Times ah because that's the bedrock of the dark matter mapping effort. We'll get more into this, but basically, EUCLID finds dark matter by looking for tiny distortions in the shapes of background galaxies caused by gravitational.

Lenses right the like it's bent by unseen mass exactly.

So, if your synthetic universe, your simulation doesn't have accurately modeled galaxy shapes to begin with, you can't properly train the software that's supposed to detect those lensing distortions in the real data, So.

You wouldn't know if your software was working correctly.

Precisely, you couldn't accurately and interpret the real observations. Those four hundred properties are what tie the simulation directly to the specific physics and the specific measurements EUCLID is designed to.

Make okay and tracing back even further before they even put the galaxies in the underlying structure, the cosmic web, that came from an even bigger calculation, didn't it.

Oh yes, before populating it with galaxies, the simulation first built the large scale structure, the scaffolding of the universe, you could say, the dark matter halos and the filaments.

Connecting them, the cosmic web.

That's it, and that structure was built by tracking the gravitational interactions. Wait for it, four trillion particles, four trillion trillon, four trillion individual points, each representing a chunk of mass pulling on every other chunk over billions of years of simulated cosmic time.

That number just defines the sheer massive scope of that initial n body calculation. You're modeling every significant lump and bump in matter density across this huge volume of space, simulating gravity's effect over cosmic history.

Think about the memory needed just to store the position and velocity of four trillion points at any given moment, let.

Alone calculate all the forces between them constantly exactly.

It's a staggering computational.

Feat and managing that four trillion interacting particles in one giant calculation that's almost beyond imagining. It needed serious computing power, right institutional scale. Where did they actually do this?

Yeah, this monster calculation was run back in twenty nineteen. They used the piz Dint supercomputer at the Swiss National Supercomputing Center CSCs and Lugano, Switzerland.

PiZZ Date wasn't that one of the fastest in the world at the time It was.

Indeed, in twenty nineteen, PiZZ Date was ranked the third most powerful supercomputer on the planet.

Okay, and to give people context on what that means in terms of resources, the sources really highlight the commitment involved. This wasn't just like a big job they ran overnight.

No far from it. Get this, more than eighty percent of PiZZ Dant's entire capacity was dedicated solely to this one calculation.

Eighty percent. Imagine basically shutting down almost everything else happening at a major national supercomputing center just to run one simulation.

It shows you how important this was and just how challenging it was computationally. Statle himself said, and I'm quoting here, it was a huge challenge to simulate such a large portion of the universe at this resolution in a single calculation.

Because it had never been done before. They needed that massive, sustained dedication of resources.

Absolutely, it pushed the limits.

So it's important for people listening to understand this two step process. Right First, the pure physics track, those four trillion particles, let gravity shape the cosmic web over billions of years. That builds the underlying framework.

Yes, the scaffolding, the dark matter distribution. That's step one. Step two is then more astrophysical and arguably more complex in some ways. Once you have those structures, the dark matter halos where galaxy should form the connecting filaments, you then have to populate them with synthetic galaxies. And these

So they look like real galaxies to the telescopes, cameras and spectrographs.

Exactly complete with those four hundred properties for each of the three point four billion galaxies. This is what produces that realistic blueprint Statle talks about a simulation of what yuclind will actually see when it looks to the sky.

So the gravitational physics creates the container, the large scale structure, and then the astrophysics fills that container with the right kinds of objects, the things EUCLID is actually looking for, the light sources. That interplay between modeling the invisible dark matter structures and the visible galaxies is really the genius of Flagship two.

It bridges the gap between fundamental theory and actual observation, which brings us neatly to the crucial why why go to all this trouble? Why dedicate eighty percent of one of the world's fastest supercomputers to building a fake universe?

And the answer really comes down to the sheer amount of data that the real EUCLID mission is generating.

Exactly, let's quickly ground ourselves in what EUCLID is doing. It's an ESA space telescope launched in June twenty twenty three started its survey, and it's designed to look at a huge chunk of the sky over a third of the entire celestial sphere, with unprecedented resolution both in imaging and spectroscopy.

That combination of vast area and sharp detail that's the source of the problem, isn't it the data logistics problem?

It is. Julian Adamik, one of the key collaborators on Flagship two, puts it very clearly. He explains that EUCLID produces data in such sheer volume and speed that having humans analyze it manually is just completely impossible.

Give us a sense of that impossibility. You mentioned three point four billion galaxies in the simulation right now.

Imagine trying to process the real data for billions of galaxies and they have Hypothetically, a highly trained astrophysicist could analyze everything about one galaxy in just one.

Second, which is ridiculously fast, totally unrealistic.

Yeah, but even then it would take centuries of NonStop work just to get through the catalog once. And EUCLID is delivering this data continuously day after day.

Wow, that's yeah, that's a staggering bottleneck. It almost feels a bit demoralizing. Scientists build this incredible instrument that produces data they know they can't possibly look at themselves.

Well, it's just the focus. The human element moves away from clicking on individual galaxies towards designing the algorithms the automated systems that can handle the load. And this is why the mock data, the Flagship two simulation is absolutely crucial.

Because you need something to test those algorithms on before the real, precious unique data arived.

Exactly, you have to develop and rigorously test the methodology for interpreting these massive, complex data sets in advance. You get afford to wait for the real data stream to start and then begin figuring out how to process.

We just fall hopelessly behind.

Hopelessly you need to be ready to process it almost instantly, often within hours or days of it coming down from the telescope because the data pipelines have to run continuously to keep up.

So the simulation Flagship two becomes the ultimate training ground. It's the perfect controlled environment for the AI and machine learning algorithms that have to do the heavy lifting.

That's a great way to put it. If the simulation is this perfect theoretical universe based on known physics, scientists can feed that simulated data into their algorithms.

And see if the algorithms correctly identify all three point four billion fake galaxies and measure their four hundred properties accurately.

Yes, And just as importantly, see if the algorithms correctly identify and handle the errors and complexities they'll encounter in real.

Data, like what kind of errors.

Things like instrumental effects, noise cosmic rays hitting the detector, but also tricky astrophysical things like accurate measuring the shapes for lensing, or dealing with deeplending objects. Yeah, where two or more galaxies overlap in the image on the sky and the software has to figure out that they're separate objects and measure their properties individually.

That's really hard, I can imagine. So if your mock data Flagship two is this perfect high resolution replica where you know the ground truth, you know exactly where every galaxy is and what its property should be, you know the right then you can run your software on it, see where it makes mistakes, and fine tune it until it reaches the accuracy and reliability needed for the real mission.

Exactly, you calibrate your tools on the simulator before you use them on the real sky.

And this also helps tackle that huge problem in astronomy systematic errors. When you look at the real universe and find something weird, how do you know if it's genuinely new physics or just some subtle bias in your telescope or your analysis software.

Flagship two helps isolate that if your algorithm process is the perfect synthetic universe and consistently finds, say that it underestimates the distances to small, faint galaxies, then you know that bias comes from the algorithm itself or how it interacts with simulated instrument effects, not from some strange new property of the actual universe. You can then fix the code before you let it loose on the real euclid data.

It really underscores how modern astronomy isn't just about pointing telescopes anymore. It's deeply intertwined with sophisticated data science, computer science, preemptive coding.

Absolutely, the modern astronomer, especially on these big survey projects, is often as much a data pipeline manager and algorithm developer as they are a traditional observer. That simulation isn't illuxiery, it's a necessity. It's like a massive digital dress rehearsal, or maybe a digital safety net.

Okay, now we get to what, for many people is the really exciting part, the potential scientific drama, because while Flagship two is fundamentally this incredible technical exercise in preparation.

Data handling, algorithm testing, its.

Ultimate scientific purpose is actually to challenge our current understanding of the universe, to actively seek out the flaws in our best theories.

It really is a high stakes test, and it's essential for listeners to remember, as we discussed that Flagship two, the mock universe is built entirely on the foundation of the standard cosmological model, which.

Is called Lambda CDM. Right, can we just quickly recap what that model actually assumes?

Sure, land to CDM basically says the universe is made up of a few key ingredients governed by Einstein's general relativity. There's ordinary matter like us, stars, gas, but much more importantly, there's cold dark matter.

CDM, invisible slow moving.

Stuff exactly invisible, doesn't interact with light, moves relatively slowly, and its gravity dictates where structures like galaxies and galaxy clusters form. That's the CDM part, okay, and the lambda landis represents the cosmological constant. This is the simplest mathematical form of dark energy, an intrinsic energy of space itself that causes the expansion of the universe to accelerate.

So dark mata pulls things together, dark energy pushes things apart, basically in simple terms.

Yes, and this LAMB to CDM model, despite its weird ingredients, has been incredibly successful. It explains a vast range of observations from the cosmic microwave background radiation left over from the Big Bang right up to the large scale distribution of galaxies we see today.

So Flagship two simulates a perfect LAMB to CDM universe, and the expectation as both statal and atomic mention is that euclid's actual observations will broadly speak and confirm the matter distribution predicted by Flagship two. That's the baseline, hope or expectation.

That's the baseline. But science rarely progresses just by confirming what we already think we know. The real excitement lies in the potential for disagreement. Scientists are actively hoping to find places where the model breaks down.

They explicitly anticipate surprises and on a expected discoveries. As the sources say, they seem quite convinced the model isn't the final word.

Well, yes, because there are already hints tensions in existing data. Statle uses that striking phrase, we already see indications of cracks in the standard model.

That sounds significant. What sort of cracks is he likely referring to? Where are these tensions showing up already?

There are a few areas. Probably the most famous is the Hubble tension. Different methods of measuring the current expansion rate of the universe the Hubble constant are giving slightly but persistently different answers.

Measuring it locally using supernovae gives one value. Measuring it based on the early universe light gives another.

Exactly and they don't quite agree within their error bars. That's a crack.

Yeah.

There are also potential issues, though perhaps less statistically strong, with how dark matter seems to clump on smaller scales compared to LAMB to CDM predictions, or maybe slight inconsistencies in gravitational lensing measurements.

So these are subtle discrepancies found by cobbling together data from different telescopes different methods.

Right, And the hope or expectation is that EUCLID, with its huge uniform high precision survey covering billions of years of cosmic time in one.

Go, might finally provide data clear enough and statistically powerful enough to show definitively whether these cracks are real and maybe reveal new phenomena that LAMB to CDM just cannot explain, things about the cosmic web structure or galaxy evolution that don't fit the simple.

Rules that tension really defines the scientific knife edge. Here Atomic sums it up perfectly. It will be exciting to see whether the model holds up against euclid's high precision data, or whether we uncover signs of new shortcomings.

So Flagship two acts as the perfect theoretical benchmark. It's the idealized prediction. EUCLID delivers the messy, complicated.

Reality, and of that reality, the EUCLID data deviates significantly and systematically from the flagship two prediction.

You can't just blame the simulation because you know exactly what physics went into it standard Lambda CDM precisely.

The blame has to fall on the input physics. The assumptions of land to CDM must be wrong or at least incomplete.

And that deviation that confirmed crack. That's where the scientific revolution would have to start.

Absolutely. It would force cosmologists back to the drawing board. Do we need more complex form of dark energy? Does dark matter have some unexpected properties? Does gravity itself behave differently on cosmic scales than Einstein predicted?

Maybe considering alternative theories like mond, modified Newtonian dynamics or other modified gravity ideas.

Potentially Yes, the simulation, by providing that precise baseline, clarifies exactly where and by how much the standard model is failing. It points the way for the next generation of theories.

Okay, let's zoom in on one of those potentially revolutionary areas, dark energy it's arguably the best mystery in cosmology. And within that standard LAMB to CDM model that Flagship two is built on, dark energy is represented in the simplest possible way.

Right exactly as Statle puts it quite bluntly. In the model, dark energy is just a constant. It's the cosmological constant Lambda, meaning it's unchanging, unchanging in space, unchanging in time. It's treated as an intrinsic property of space itself, a constant energy density everywhere that doesn't dilute as the universe expands. It's the simplest mathematical term you can add to Einstein's equations to get accelerated expansion.

But the whole point of EUCLID, one of its primary missions is to rigorously test whether that assumption holds true. Is it really just a constant? Why is finding that out so critical?

Because if it's not a constant, if its strength or density has changed over cosmic history, then it's not just some background property of space time. It must be something dynamic, something physical that evolves.

Which would completely change our picture of the universe's past and presumably its future fate.

Absolutely, if it's a true constant Lambda, the acceleration continues relentlessly, leading eventually to a big freeze or maybe even a big rip where everything gets torn apart. But if dark energy is dynamic, if its strength changes, well, all bets are off. It could fade away, could strengthen, It could oscillate.

And isn't this where theoretical ideas like quintessence come in?

Yes, exactly. Quintessence is the general name physicists give to hypothetical dynamic forms of dark energy, usually pictured to some kind of slowly evolving scalar field spread throughout space, a bit like the field that might have driven inflation in the very early universe, but operating now.

So finding evidence that lambda isn't constant would be huge. It would mean moving from this simple placeholder to needing a whole new physical theory of this dynamic field.

Precisely, it opens up a whole new realm of fundamental physics.

So how does EUCLID actually test this? How can it tell if dark energy was the same strength billions of years ago as it is today.

It comes back to the sheer scale and depth of its survey. EUCLID is mapping the positions and distances of billions of galaxies across a huge volume of space looking back up to ten billion years in cosmic time.

So it's effectively creating snapshots of the universe at different ages.

That's a good way to think of it. By measuring galaxies at different look back times, astronomers can reconstruct the history of cosmic expansion.

How do they measure the expansion rate at those past times? Is it just about how clustered the galaxies are.

It's primarily done through measuring distances and redshifts very precisely. Redshift, as you know, is the stretching of light's wavelength as it travels through the expanding universe.

The farther away something is, the more its light is stretched, the higher it's red shift.

Correct, and the relationship between distance and redshift isn't constant. It depends on how fast the universe was expanding at different times in the past. Euclid uses techniques like measuring baryon acoustic oscillations characteristic patterns in how galaxies cluster as a sort of standard ruler to get precise distances at different redshifts.

Ah okay, So by measuring these standard rulers at various distances and thus various past times. They can chart the expansion history.

Precisely ademic frames the core question they're asking. We can see how the universe expanded at that time, looking back billions of years, and measure whether this constant really remained constant. They're literally mapping the acceleration rate over cosmic time.

And if that acceleration rate was different, say five billion years ago compared to ten billion years ago, or compared to it today.

Then LAMB is not a constant. Game over for the simplest model, that would be the smoking gun evidence that we need new physics beyond standard LAMB to CDM to explain dark energy.

Now, Statle does add a note of caution rate. He suggests you could might not deliver the final definitive answer on dark energy overnight.

Yeah, he manages expectations a bit. It's unlikely to be a single Eureka moment from one data release. Understanding dark energy is incredibly challenging, but he expresses strong confidence that you could will bring us a step closer to understanding the mysterious realm of dark energy.

It's about gathering that unprecedentedly precise day data and needed to really start ruling out possibilities. Can we rule out the simple constant. If so, what kind of dynamic behavior does the data favor exactly?

And again, having flagship two, which was built assuming lambda as a constant, provides that perfect reference point. Any deviation UCLI finds in the actual expansion history will stand out starkly when compared to the simulation's prediction. It sharpens the search.

So to achieve these really ambitious goals testing the constancy of dark energy, mapping dark matter, looking for cracks in the standard model, EUCLID relies on some pretty sophisticated observational techniques. We should probably touch on those. It's not just taking pretty pictures.

Definitely not. It's about precision and scale combined. It really is the most comprehensive survey ever attempted in terms of both the volume covered and the detail captured within that volume.

Okay, let's start with mapping the invisible stuff dark matter. You mentioned gravitational lensing earlier. How does EUCLID actually use that?

Right? So, EUCLID has incredibly high spatial resolution, meaning it can take really sharp images. This allows its researchers to detect very subtle distortions in the shapes of literally billions of distant background galaxies.

These aren't the dramatic arcs and multiple images you see from strong lensing around massive galaxy cluster.

No, this is weak lensing. The effect is tiny, just a slight statistical preference for galaxies behind a massive structure to appear slightly stretched or sheared in a particular direction.

Like looking through a very subtly warped window pane.

As you say, exactly, and the warping the distortion is caused by the gravitational pull of all the mass lying between us and those background galaxies, primarily the invisible clumps and filaments of dark matter that make up the cosmic web.

So by measuring these tiny systematic distortions across billions of galaxies spread over the sky.

Scientists can essentially reverse engineer a map of where the intervening mass must be. They can figure out the distribution of the stuff doing the bending, even.

Though they can't see the dark matter directly.

Correct The result is a massive three dimensional map of the dark matter distribution across a huge portion of the universe. This map is crucial for testing the predictions of Flagship two, which remember simulated precisely how dark matters should be distributed according to lambda CDM physics.

Okay, so weak lensing gives you the map of the mass distribution, but you need to know where things are in three D space, especially their distances, to make sense of cosmic evolution and expansion history. How does EUCLID get the distances.

That's where the complementary technique comes in spectroscopy. EUCLID has an instrument called NISP near ineferred spectrometer and photometer that can measure the spectrum of light from millions upon millions.

Of galaxies, breaking the light down into its constituent colors like a prism does.

Exactly, and by doing that, scientists can measure the galaxy's redshift with very high accuracy. They look for characteristic features in the spectrum absorption or emission lines from specific chemical elements and see how much they've been shifted towards words, longer, redder wavelengths due to cosmic expansion.

And as we establish, redshift is the key indicator of distance in an expanding universe. More redshift means farther away and further back in.

Time, precisely so, by getting these accurate spectroscopic redshifts for a huge number of galaxies euclib can pinpoint their distances with unprecedented accuracy. This is what allows them to slice the universe up into those different timebins we talked about to map the expansion rate over ten billion years.

So it's the combination the two techniques that's really powerful. The sharp imaging gives you the galaxy shapes for weak lensing mapping mass, and.

The sectrocity gives you the precise red shifts for distances mapping expansion history and three D position.

That's the synergy. You put them together and you build up this incredibly detailed three dimensional map of both the visible galaxies and the invisible dark matter structures spanning this enormous cosmic sphere with a radius of ten billion light years, a survey of cosmic structure and evolution covering more than two thirds of the universe's entire history.

Just staggering. And there's one more benefit of casting such a wide net, isn't there The possibility of finding things you aren't even looking for.

Ah.

Yes, the element of serendipity. Because euclid is surveying such a truly vast volume of space, the chances of stumbling upon extremely rare or unexpected objects or phenomena are actually quite high.

A Domic mentions this right, that some things are just intrinsically extremely uncommon. Maybe a specific type of superluminous supernova, or a weird kind of quasar, or a particular stage of galaxy merging that only happens fleetingly.

Exactly in any small patch of sky. You might never see one. But when your survey volume encompasses billions of light years, you're bound to catch some of these rare events just by sheer statistical luck.

That's the idea. Adamic says. The chances of finding unexpected or rare objects are high. This is pure discovery space. You might find objects or phenomena that challenge existing theories in ways nobody even anticipated, simply because nobody had looked wide enough and deep enough before. Hashtag tag tag outro so to kind of wrap this all up. This incredible

Okay, let's recap them first.

First, it's the essential data preparation engine. It allowed scientists to develop and test the automated software pipelines needed to actually handle euclid's massive data volume. Without it, they'd be drowning.

Got it. Second.

Second, it's the highest precision test bed ever created for the standard LANDB to CDM model of cosmology. It's the perfect theoretical prediction against which the real universe will be.

Measured looking for those cracks.

And third and third, building on that, it serves as the indispensable guide for interpreting the complex cosmic history that EUCLID is now observing. By comparing the real data to the simulation, scientists can better understand the growth of structure, thetion of galaxies, and the nature of dark energy over time.

And this isn't just theoretical anymore. Things are happening. You mentioned the first data release, the quick data release that came out in March twenty twenty five.

That's right. Even that was just a small taste relatively speaking of a full survey, but it already provided new insights, particularly into galaxy clusters and the structure of the nearby cosmic web, and papers based on that and related simulation work are already being published in journals like Astronomy and Astrophysics.

And there's a plan for more data coming soon.

Yes, the next major planned data release, which will be significantly larger, is expected sometime in spring twenty twenty six. So the pace is really picking up.

It's a rapid unfolding of discovery where every step relies heavily on that groundwork laid by the simulation. Those processing pipelines tested on Flagship two, they're running now on the real data.

Absolutely, the preparation is paying off.

What really stands out to me thinking about the whole project is the almost philosophical intent behind Flagship two. Here we have the most sophisticated, detailed digital model of reality ever created, built explicitly not just to confirm what we think we know, but specifically to find the errors in our understanding. It's like building a perfect machine designed to highlight its own potential flaws when compared to the real thing.

That's a great way to put it. It's a theoretical construct designed in a sense to be proven wrong or at least incomplete, in the most informative way possible.

And that sets up this fascinating tension that you listening should really keep in mind. As the euclid results continue to roll in over the next few years exactly.

You've got this perfect, pristine, synthetic universe inside Flagship two, totally controlled high resolution built strictly on the rules of LAMB to CDM physics. And then you have the messy, noisy complex real data arriving from the EUCLID telescope, full of unexpected glitches, statistical fluctuations, and potentially truly baffling outliers.

So the big question becomes if the broad strokes match. If the for all map of the cosmos looks roughly like the simulation predicts, but there are these nagging discrepancies, maybe dark matter clumps slightly differently in nearby voice and expected, or maybe a few of those super rare unpredicted objects keep popping up. Which do you trust more?

Do you emphasize the broad confirmation that seems to reinforce decades of established physics, or do you focus on those unexpected outliers, the cracks, the things that don't fit the model and seem to demand entirely new.

Theories that tension right there between the expected pattern and the surprising detail. That's where the future of cosmology is being forged. That's where the discoveries will likely lie.

Absolutely it's an exciting time to be watching. Thanks for joining us for this exploration.

We'll see you next time.

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