Speaker 1: Welcome to Bedtime Astronomy. Explore the wonders of the cosmos
Speaker 1: with our soothing Bedtime Astronomy 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: Have you ever considered the inherent bias in how we
Speaker 2: observe the universe.
Speaker 3: It's a massive bias, right.
Speaker 2: I mean, when you look up at the night sky,
Speaker 2: you are basically experiencing a severely filtered version of reality, just.
Speaker 3: A tiny fraction of what's out there exactly.
Speaker 2: You see the stars, the planets, maybe the Andromeda Galaxy.
Speaker 2: If you are in a dark enough location, you're lucky. Yeah,
Speaker 2: if you're lucky. But fundamentally, human eyes, and by extension,
Speaker 2: the vast majority of our optical telescopes, they are drawn
Speaker 2: exclusively to the brightest sources of light.
Speaker 3: It's a foundational paradox in astrophysics, really.
Speaker 2: It is. We build our entire models of the cosmos
Speaker 2: based on these blazing beacons in the dark. You know,
Speaker 2: the massive, highly luminous galaxies and quasars.
Speaker 3: But doing that, relying just on the bright spots leaves
Speaker 3: a monumental blind spot, a.
Speaker 2: Huge one, because what about the vast spaces between those objects,
Speaker 2: seemingly empty voids, because they are not empty at all,
Speaker 2: far from it. So today we are doing a deep
Speaker 2: dive into a groundbreaking publication from the Astrophysical Journal. This
Speaker 2: was released very recently March three, twenty twenty six.
Speaker 3: Based on some incredible.
Speaker 2: Data, yes, data from the hobby Everly Telescope Dark Energy Experiment,
Speaker 2: which will refer to as HIA dectics. Our mission today
Speaker 2: for you, the listener, is to understand how astronomers are
Speaker 2: finally looking past those bright stars.
Speaker 3: They're finding a hidden ocean, basically.
Speaker 2: A vast, hidden sea of light residing right between really galaxies.
Speaker 2: And we'll explore what mapping this invisible cosmic web actually
Speaker 2: means for our fundamental understanding of the universe.
Speaker 3: This research really represents a complete paradigm shift in observational cosmology.
Speaker 3: Ow so well, we are looking at a highly critical
Speaker 3: epoch in the universe's history, specifically the period between nine
Speaker 3: and eleven billion years ago.
Speaker 2: Okay, let's unpack this. Why are astronomers so heavily focused
Speaker 2: on this particular window. What makes the universe of ten
Speaker 2: billion years ago so crucial for understanding the galaxies we
Speaker 2: see around us today.
Speaker 3: That timeframe, it corresponds to what astrophysicists often call cosmic.
Speaker 2: Noon, cosmic noon. I love that term.
Speaker 3: It's very descriptive. If you look at the cosmic star
Speaker 3: formation rate history, it.
Speaker 2: Isn't a flatline, right, not at all.
Speaker 3: The universe did not produce stars at a constant rate.
Speaker 3: About nine to eleven billion years ago, the universe was
Speaker 3: experiencing its absolute peak of star formation.
Speaker 2: So it was just churning out stars.
Speaker 3: It was an incredibly dynamic, violently active epoch. The galaxies
Speaker 3: during this period, they weren't the settled, beautiful spiral galaxies
Speaker 3: we see today.
Speaker 2: Like the Milky Way, right.
Speaker 3: They weren't like the Milky Way. They were chaotic. They
Speaker 3: were actively pulling in or accreting massive amounts of primordial
Speaker 3: gas from the intergalactic medium, just feeding on this gas
Speaker 3: exactly and igniting stars at rates hundreds or sometimes thousands
Speaker 3: of times higher than what our galaxy does today.
Speaker 2: So it's essentially the peak of cosmic construction. But observing
Speaker 2: that construction comes with significant physical limitations, doesn't it very significant?
Speaker 3: The primary limitation is just surface.
Speaker 2: Brightness because it's so far away, right, Because.
Speaker 3: We're looking at objects nine to eleven billion light years away,
Speaker 3: the inverse square law dictates that the light reaching us
Speaker 3: is exceptionally faint.
Speaker 2: Okay, but we can see some galaxies from back then.
Speaker 3: We can resolve the most massive hyperluminous galaxies from that era, yes,
Speaker 3: because their localized starburst activity is so incredibly intense. But
Speaker 3: the smaller stuff, the fainter dwarf galaxies, and more importantly,
Speaker 3: the sprawling filaments of fuse hydrogen gas that form the
Speaker 3: cosmic web.
Speaker 2: The fuel lines.
Speaker 3: Basically right the fuel lines, they fall well below the
Speaker 3: detection limits of standard optical imaging. The light from that
Speaker 3: diffuse gas has spread out over billions of light years.
Speaker 2: By the time it hits a telescope mirror on Earth.
Speaker 3: It's effectively indistinguishable from the ambient background noise of the
Speaker 3: night sky, or even the thermal noise of the camera
Speaker 3: instruments themselves.
Speaker 2: The time machine aspect of this is something that always
Speaker 2: strikes me. We use that term colloquially, but mathematically that
Speaker 2: is exactly what telescope data from this era represents.
Speaker 3: It's literally a time machine, right.
Speaker 2: Because the speed of light is a hard limit. When
Speaker 2: we pull data from ha tex that originated ten billion
Speaker 2: years ago, we are capturing photons that have been traveling
Speaker 2: through the vacuum of space since before our sun even existed.
Speaker 3: Long before the Earth is roughly four and a half
Speaker 3: billion years old.
Speaker 2: So this light had already been traveling for over five
Speaker 2: billion years before our planet even coalesced from a cloud
Speaker 2: of dust. It's staggering to think about it really is,
Speaker 2: and capturing that specific ancient light to reconstruct those early
Speaker 2: fuel lines, it requires an entirely different approach than just
Speaker 2: taking a long exposure photograph.
Speaker 3: Right, you get to take a picture. Taking a standard
Speaker 3: optical image of this epoch to find diffuse gas is
Speaker 3: largely feudile.
Speaker 2: Because the background washes it out.
Speaker 3: Exactly The broad band filters used in standard photography or
Speaker 3: photometry simply let into much background light. It washes out
Speaker 3: any faint structures.
Speaker 2: So what's the alternative to.
Speaker 3: Map the intergalactic gas ten billion years ago? We have
Speaker 3: to abandon images completely. We rely almost entirely on spectroscopy.
Speaker 2: We're not looking for the physical shape of a galaxy anymore.
Speaker 3: No, we are looking for the highly specific physical signatures
Speaker 3: hidden within the electromagnetic radiation itself. We isolate distinct wavelengths
Speaker 3: of light that prove the presence of specific matter.
Speaker 2: That requires transitioning our focus to the actual language of light,
Speaker 2: the spectrum. Now for you listening, breaking light down into
Speaker 2: a spectrum reveals the emission and absorption lines of chemical
Speaker 2: elements like.
Speaker 3: A cosic barcode, right, a barcode.
Speaker 2: But I want to zero in on the specific wavelength
Speaker 2: that makes this entire HGDX map possible. The Liman alpha emission.
Speaker 3: Line the holy grail for this era.
Speaker 2: Why is this specific quantum transition the ultimate tool for
Speaker 2: looking at the cosmic noon?
Speaker 3: Well, the Limon alpha line is the cornerstone of high
Speaker 3: redshift observational astronomy. To understand why, we just need a
Speaker 3: quick look at the quantum mechanics of the hydrogen atom.
Speaker 2: Which is the most abundant element out there by far.
Speaker 3: So when a hydrogen atom sits near a region of
Speaker 3: intense star formation, like those massive chaotic galaxies we talked about,
Speaker 3: it gets bombarded by extreme ultraviolet.
Speaker 2: Radiation from the young hot stars.
Speaker 3: Yes, specifically massive o and B type stars. This radiation
Speaker 3: is so energetic that ionizes the hydrogen. It physically strips
Speaker 3: the electron away from the proton, and.
Speaker 2: The signal we're looking for is created when that electron
Speaker 2: finds its way.
Speaker 3: Back right correct, when the pro eventually recombines with an electron,
Speaker 3: that electron cascades down through the atom's specific quantized energy.
Speaker 2: Level, well stepping down a ladder, exactly.
Speaker 3: Like stepping down a ladder, and when it drops from
Speaker 3: the first excited state down to the grounds the bottom
Speaker 3: run of the ladder, it releases a photon with a
Speaker 3: very specific, unchangeable.
Speaker 2: Wavelength, and that wavelength is one hundred.
Speaker 3: And twenty one point six nanometers. That is the Liman
Speaker 3: alpha line.
Speaker 2: We one hundred and twenty one point six.
Speaker 3: Okay, Because the early universe was absolutely dominated by hydrogen
Speaker 3: and the star formation rates were so extreme, these galaxies
Speaker 3: act as colossal Liman alpha factories. They pump out an
Speaker 3: astonishing number of these specific photons.
Speaker 2: But wait, one hundred twenty one point six nanometers is
Speaker 2: deep in the ultraviolet spectrum that's invisible to the human eye.
Speaker 2: It is, and it doesn't stay at one hundred and
Speaker 2: twenty one point six nanimeters either because of the expansion.
Speaker 3: Of the universe right the red shift.
Speaker 2: As those photons travel through space for ten billion years,
Speaker 2: space itself expands, it physically stretches the light.
Speaker 3: Waves, so by the time they reach the HGTX instruments
Speaker 3: in Texas, that ultraviolet light has been red shifted straight
Speaker 3: into the visible optical bands right.
Speaker 2: Around three hundred and fifty to five hundred and fifteen
Speaker 2: nanimeters greenish blue light exactly. The source material notes this
Speaker 2: shows up as a dramatic peak in the data. If
Speaker 2: you picture the spectrographic feed, you have this relatively flat
Speaker 2: continuum of background emission and then boom, a violent, unmistakable.
Speaker 3: Spike at that specific red shifted wavelength.
Speaker 2: It's the undeniable fingerprint of excited hydrogen at that exact
Speaker 2: distance in the universe.
Speaker 3: We call that dramatic peak a Liman alpha emitter or
Speaker 3: an lae An lee. Finding those massive spikes is the
Speaker 3: traditional methodology for locating high red shift galaxies. If you
Speaker 3: see that peak, you have definitively located a bright active
Speaker 3: galaxy from that specific epoch.
Speaker 2: But the galaxies aren't the whole story.
Speaker 3: No, theoretical models of the cosmic web have always suggested
Speaker 3: that lemon alpha mission shouldn't just be restricted to the
Speaker 3: massive galaxies.
Speaker 2: The gas in between them should be glowing too.
Speaker 3: The vast filaments of intergalactic gas drifting between the galaxies
Speaker 3: should also be emitting these photons, either through recombination from
Speaker 3: the background radiation or just from gas falling into dark
Speaker 3: matter halos and heating up.
Speaker 2: But the emission from those gas filaments that would be
Speaker 2: orders of magnitude weaker than the galaxies themselves.
Speaker 3: Vastly weaker. I mean, the peaks from the galaxies are
Speaker 3: highly localized, they're relatively easy to extract from the data.
Speaker 2: They stand out.
Speaker 3: They do, but the emission from the intergalactic gas is
Speaker 3: exceptionally diffuse. It's just a subtle, incredibly faint glow spread
Speaker 3: across massive cosmic volumes.
Speaker 2: And for decades, finding that faint signal was thought to
Speaker 2: be impossible.
Speaker 3: Virtually impossible on a large scale. The instrumental noise, the
Speaker 3: foreground light from our own solar system, it all drowns it.
Speaker 2: Out, which brings us to the sheer scale of the
Speaker 2: instrument required to even attempt this. You cannot just point
Speaker 2: a standard observatory telescope at the sky and hope to
Speaker 2: map this stuff.
Speaker 3: No, you need a behemoth.
Speaker 2: You need the hobby Eberly Telescope Dark e Energy Experiment
Speaker 2: at the McDonald Observatory in West Texas. Let's discuss the
Speaker 2: volume of this survey, because the engineering reality of headdecks
Speaker 2: is staggering the hobby.
Speaker 3: Every telescope. The HET is a totally unique piece of engineering.
Speaker 2: Most big telescopes move on dual axis, right, They tilt
Speaker 2: up and down and spin around to track the sky.
Speaker 3: Right. But the HT has a fixed elevation angle. It
Speaker 3: sits permanently at fifty five degrees.
Speaker 2: Okay.
Speaker 3: It simply rotates in asimuth around in a circle while
Speaker 3: a highly complex tracker moves across the focal plane at
Speaker 3: the top to follow the astronomical targets as the Earth turns.
Speaker 2: That's incredibly clever.
Speaker 3: It's very efficient, and for the dark energy experiment, the
Speaker 3: telescope was upgraded with a massive array of spectrographs.
Speaker 2: They call it VIRUS, the Visible Integral Field Replicable Unit spectrograph.
Speaker 3: That's the one, and VIRUS isn't just one single instrument, No.
Speaker 2: It's a massive replication strategy. Instead of building one giant spectrograph.
Speaker 2: They build dozens of identical units and fed them with
Speaker 2: thousands of optical fibers.
Speaker 3: Over thirty thousand optical fiber thirty thousand. This allows eighth
Speaker 3: decics to perform integral field spectroscopy on an industrial scale.
Speaker 3: They're taking spectra of thousands of discrete points on the
Speaker 3: sky simultaneously.
Speaker 2: And their primary mission, as the name implies, is mapping
Speaker 2: the expansion history of the universe to constrain dark energy.
Speaker 3: Yes, their stated goal was to chart the three D
Speaker 3: positions of over one million bright Liman alpha emitting galaxies.
Speaker 2: To get that catalog of a million galaxies, they had
Speaker 2: to cover a massive area of the sky, an area
Speaker 2: measuring over two thousand full moons.
Speaker 3: It's a huge swathe of the celestial sphere.
Speaker 2: Let's help you visualize that the angular diameter of the
Speaker 2: full moon is about half a degree, so taking up
Speaker 2: two thousand full moons, that is a massive, sweeping expanse
Speaker 2: of space.
Speaker 3: They're just blindly pointing these thirty thousand fibers at the sky,
Speaker 3: pulling in light, separating into wavelengths, and generating an unbelievable
Speaker 3: six hundred million individual spectra.
Speaker 2: He Here's where it gets really interesting. Carl Gipart, the
Speaker 2: principal investigator for at DENEX, revealed a metric about this
Speaker 2: data collection that fundamentally alters how we view these surveys.
Speaker 3: It really does.
Speaker 2: Despite gathering six hundred million spectra that primary dark energy mission,
Speaker 2: the effort to map the one million bright galaxies, it
Speaker 2: only utilizes roughly five percent of the collected data.
Speaker 3: Five percent. That is the crucial pivot point of this
Speaker 3: entire deep dive. It's what the primary pipeline for h
Speaker 3: DEX is designed for point source extraction. It scans all
Speaker 3: six hundred million spectra looking for high signal to noise ratio.
Speaker 2: Peaks the bright galaxies.
Speaker 3: Exactly once it identifies in catalogs at Galaxy. The rest
Speaker 3: of the data surrounding that peak, which makes up ninety
Speaker 3: five percent of the total data set, is mathematically categorized
Speaker 3: as background noise.
Speaker 2: They threw out ninety five percent of the data, I
Speaker 2: mean not literally deleted it from the hard drives, but
Speaker 2: scientifically it was sideline was ignored. You build this incredibly
Speaker 2: complex array of thirty five optical fibers, you survey two
Speaker 2: thousand full moons of sky and ninety five percent of
Speaker 2: the photons you catch are deemed irrelevant just because they
Speaker 2: don't cross a specific brightness threshold.
Speaker 3: Well, from a traditional survey perspective, that is standard operating procedure. Really, yes,
Speaker 3: if your objective is a highly pure catalog of discrete
Speaker 3: individual objects, anything that cannot be confidently resolved as an
Speaker 3: object is an impediment.
Speaker 2: It's just getting in the way, exactly.
Speaker 3: It's four ground light, it's atmospheric air glow, it's thermal
Speaker 3: noise in the CCD detectors.
Speaker 2: But Masha Lujah Niemeyer and the team behind this new
Speaker 2: publication they recognized a profound philosophical flaw in that approach.
Speaker 3: They realized that ninety five percent is not empty noise,
Speaker 3: no among.
Speaker 2: The instrument artifacts, and the air glow is the literal
Speaker 2: sea of light from the cosmic web.
Speaker 3: It contains the aggregate liman alpha emissions of all the
Speaker 3: dwarf galaxies that were too faint to trigger the detection algorithms,
Speaker 3: plus the glowing films of intergalactic gas.
Speaker 2: It's the difference between mapping the peaks of a mountain
Speaker 2: range and mapping the entire tectonic plate underneath it.
Speaker 3: That's a great way to put it.
Speaker 2: The bright galaxies are just the most luminous nodes of
Speaker 2: a much larger, interconnected structure. But the challenge wasn't getting
Speaker 2: the data hadx already banked half a petabite of it.
Speaker 2: The challenge was statistical, highly statistical. How do you extract
Speaker 2: an incredibly faint, highly diffused signal from a data set
Speaker 2: where the noise is orders of magnitude louder than the signal.
Speaker 3: You have to completely abandon the concept of object resolution.
Speaker 2: Stop looking for individual things exactly.
Speaker 3: You can no longer ask the data pipeline to find
Speaker 3: a specific galaxy. This requires transitioning to a technique known
Speaker 3: as line intensity mapping.
Speaker 2: Line intensity mapping or LIMB.
Speaker 3: WIM fundamentally redefines the objective of the survey. Instead of
Speaker 3: searching for spatial coordinates of bright peaks, LIMB measures the
Speaker 3: integrated surface brightness of a specific spectral line across large
Speaker 3: cosmic volumes.
Speaker 2: To make that concrete for you, listening, Shuleian Mignot's, a
Speaker 2: co author on the paper, offered an excellent analogy regarding
Speaker 2: how we view the spatial distribution.
Speaker 3: Of light, the airplane analogy.
Speaker 2: Yes, he compared the traditional cataloging method to flying in
Speaker 2: an airplane at night and trying to map a country's
Speaker 2: population by only looking at the brightest city centers.
Speaker 3: It's highly illustrative. If your optical censor on the aircraft
Speaker 3: is calibrated to only register the intense light output of
Speaker 3: major metropolitan areas like New York, Chicago.
Speaker 2: Los Angeles, then your resulting map implies a binary distribution.
Speaker 3: Exactly, it implies there are intense points of existence surrounded
Speaker 3: by total empty voids.
Speaker 2: But we know demographically that the population is continuous.
Speaker 3: There are sprawling suburbs, rural highway corridors, and small towns
Speaker 3: connecting those major hubs.
Speaker 2: And the traditional point source extraction of ATX was mapping
Speaker 2: the cosmic cities, but it was completely blind to the
Speaker 2: cosmic suburbs and the interstate highways of gas connecting.
Speaker 3: Them, because the light from the suburbs is isn't concentrated
Speaker 3: enough to trigger the sensor.
Speaker 2: So to map the entire landscape, Muno's suggests keeping the
Speaker 2: airplane at the exact same altitude, looking at the exact
Speaker 2: same landscape, but changing the optical properties of the sensor.
Speaker 3: You look through a deliberately smudged window.
Speaker 2: A smudged window.
Speaker 3: The smudged window represents the spatial and spectral smoothing inherent
Speaker 3: in line intensity mapping. When you apply a smoothing kernel
Speaker 3: to the data, you intentionally degrade the resolution. You make
Speaker 3: it burry, You make it very blurry. You can no
Speaker 3: longer distinguish the sharp boundaries of the major cities. The
Speaker 3: points of light blur and expand.
Speaker 2: But there's a mathematical advantage to that, right, a critical one.
Speaker 3: While resolution is lost, the total photon count is conserved,
Speaker 3: it's aggregated. The faint sub threshold light from the cosmic
Speaker 3: suburbs is integrated into larger volumetric pixels, which we call.
Speaker 2: Voxels vouxeles three D pixels right.
Speaker 3: And by integrating over larger volumes, the faint signal of
Speaker 3: the diffuse limon al for emission constructively interferes. It naturally
Speaker 3: rises above the threshold of the random, uncorrelated instrumental noise.
Speaker 2: It's a brilliant statistical maneuver. You sacrifice the ability to
Speaker 2: say there is a distinct dwarf galaxy exactly at coordinate X,
Speaker 2: but you gain the ability to say this entire region
Speaker 2: of space is radiating a faint Liman alpha glow. The
Speaker 2: blurry picture actually contains more cosmological information about the distribution
Speaker 2: of matter than the sharp, highly filtered picture did.
Speaker 3: It's a more complete truth. Now the source material does
Speaker 3: clarify that line intensity mapping itself is not a newly
Speaker 3: invented concept.
Speaker 2: Right radio astronomers have used it.
Speaker 3: They've used it for years to map the twenty one
Speaker 3: centimeter line of neutral hydrogen. But applying this technique to
Speaker 3: the ultraviolet Liman alpha e missions in the optical band.
Speaker 2: Over a survey area of two.
Speaker 3: Thousand full moons, that is entirely unprecedented.
Speaker 2: And the application at this scale introduces formidable computational complexities.
Speaker 3: Massive complexities in twenty one centimeter mapping. The foregrounds are intense,
Speaker 3: but the spectral line itself is relative straightforward.
Speaker 2: But Lyman alpha is messy.
Speaker 3: It's a resonant line. The focons scatter repeatedly off neutral
Speaker 3: hydrogen atoms before they ever escape the galactic halo. It
Speaker 3: makes the radiative transfer extremely complex to model.
Speaker 2: Plus the data set itself is gargantuan. To apply line
Speaker 2: intensity mapping to the discarded ninety five percent of the
Speaker 2: HX data required processing roughly half a petabyte of raw
Speaker 2: spectroscopic files. Half a petabyte, let's give that some scale.
Speaker 2: If you consider a high definition movie to be roughly
Speaker 2: five gigabytes, half a petabyte is equivalent to one hundred
Speaker 2: thousand high definition movies.
Speaker 3: You're not doing that on all laptop.
Speaker 2: No. Processing that volume of data isn't something you do
Speaker 2: on a workstation in a university lab. The team had
Speaker 2: to rely on the Texas Advanced Computing Center or TACC.
Speaker 3: They were utilizing supercomputers like Fronterra and Stampede.
Speaker 2: Running completely customed pipelines. They had to mathematically strip away
Speaker 2: the atmospheric emission lines, the foreground zodiacal light from our
Speaker 2: solar system, the galactic cirrus from the Milky Way, and
Speaker 2: all the instrumental artifacts.
Speaker 3: From six hundred million spectra, all.
Speaker 2: Without accidentally erasing the incredibly fragile ultra faintlyman alpha signal
Speaker 2: hidden underneath.
Speaker 3: The data reduction pipeline is an engineering marvel in itself,
Speaker 3: but raw computational power is meaningless without a rigorous physical
Speaker 3: framework to guide it. If we connect this to the
Speaker 3: bigger picture, the actual methodology used to reveal the cosmic
Speaker 3: web relies on a foundational property of.
Speaker 2: Cosmology, gravitational clustering.
Speaker 3: Exactly, the universe is not a uniform soup of matter.
Speaker 3: It is heavily structured by the gravitational potential wells of
Speaker 3: dark matter halos.
Speaker 2: And this is where Chro Komatsu's signpost technique comes into play.
Speaker 2: Kamatsu is a highly respected cosmologist at the Max Planck
Speaker 2: Institute for Astrophysics.
Speaker 3: His contribution here is brilliant. It bridges the gap between
Speaker 3: the five percent catalog and the ninety five percent noise.
Speaker 2: He utilizes the concept of cross correlation. Since gravity dictates
Speaker 2: that matter will pool inside these dark matter halos, we
Speaker 2: know that massive bright galaxies do not exist in isolation.
Speaker 3: They seated the densest nodes of the cosmic web exactly.
Speaker 2: The one million bright Lineman alpha emitters the five percent
Speaker 2: of the data already cataloged by HDX. They aren't discarded
Speaker 2: in this new.
Speaker 3: Map, far from it. They are the anchors.
Speaker 2: The anchors.
Speaker 3: In cosmology, we use a metric called the two point
Speaker 3: correlation function, which means it essentially calculates the probability of
Speaker 3: finding a specific signal at a given distance from a
Speaker 3: known reference point.
Speaker 2: Okay.
Speaker 3: Commants's signpost method leverages this by cross correlating the known
Speaker 3: three D positions of the bright galaxies the cities with
Speaker 3: the heavily smoothed faint intensity map of the remaining ninety
Speaker 3: five percent of the data the suburbs.
Speaker 2: So the supercomputer takes the coordinates of a known bright
Speaker 2: galaxy and says, based on the laws of gravity, there
Speaker 2: should be a localized over density of gas and dwarf
Speaker 2: galaxies right around.
Speaker 3: This coordinator right knows where to look.
Speaker 2: It then looks at the intensity map for that specific
Speaker 2: region and extracts the faint signal that statistically correlates with
Speaker 2: the presence of that bright anchor.
Speaker 3: The bright galaxies act as gravitational signpost shedding. Look here,
Speaker 3: the cosmic web is thickest right around me.
Speaker 2: By stacking the signals around hundreds of thousands of these signposts,
Speaker 2: something amazing happens.
Speaker 3: With the math. The random instrumental noise, which obviously does
Speaker 3: not correlate with the physical positions of the galaxies, averages
Speaker 3: out to zero.
Speaker 2: But the real astrophysical signal from the intergalactic.
Speaker 3: Gas it constructively adds up. It stacks this cross correlation
Speaker 3: technique allowed the supercomputers to mathematically triangulate and reveal the
Speaker 3: three dimensional morphology of the diffuse gas.
Speaker 2: It's incredible to visualize the output. You begin with an
Speaker 2: empty void punctuated by one million isolated, brilliant points.
Speaker 3: Of light a standard scatterplot.
Speaker 2: Exactly a scatterplot. But as the cross correlation algorithm runs
Speaker 2: across the half petabyte of data, the spaces between those
Speaker 2: points begin to glow.
Speaker 3: The massive filaments of hydrogen gas emerge from the background noise.
Speaker 2: Stretching across millions of light years, linking the bright nodes together.
Speaker 2: The cosmic suburbs are illuminated.
Speaker 3: It transforms our view of the nine to eleven billion
Speaker 3: year old universe from a collection of isolated islands into
Speaker 3: a massive, contiguous structure.
Speaker 2: So what does this all mean. The empirical observation of
Speaker 2: the structure is a monumental achievement, obviously, but why does
Speaker 2: having this empirical map matter if we already had theoretical models.
Speaker 3: That's a great question.
Speaker 2: I mean, we have massive supercomputer simulations like Illustris, TNG
Speaker 2: or the EGL project. These simulations take the initial conditions
Speaker 2: of the Big Bang, apply the laws of fluidynamics, dark
Speaker 2: matter gravity and run it forward to see how the
Speaker 2: cosmic web forms. Aren't those simulations accurate enough.
Speaker 3: Well, simulations are inherently limited by their resolution and by
Speaker 3: the assumptions encoded within their subgrid physics.
Speaker 2: Some grid physics.
Speaker 3: In a cosmological simulation spanning hundreds of millions of light years,
Speaker 3: it is computationally impossible to model the physics of individual
Speaker 3: stars or individual supermassive black holes.
Speaker 2: The scale is just too vast right.
Speaker 3: Therefore, simulators use recipes to approximate the effects of supernova
Speaker 3: feedback or active galactic nucleus agen feedback.
Speaker 2: The processes that violently eject gas out of galaxies and
Speaker 2: back into the intergalactic medium.
Speaker 3: Precisely, they have to estimate how much energy a black
Speaker 3: hole dumps into the surrounding gas because they can't simulate
Speaker 3: every photon.
Speaker 2: And if the assumptions in those feedback recipes are slightly inaccurate, then.
Speaker 3: The resulting distribution of gas in the simulation will diverge
Speaker 3: from reality. H Prior to the eight dex line intensity map,
Speaker 3: theorists could simulate the flow of gas into and out
Speaker 3: of galaxies at cosmic noon, but they lacked the comprehensive
Speaker 3: observational data to verify if their feedback models were actually correct.
Speaker 2: The hate dex map provides the ground truth is the
Speaker 2: real universe. So cosmologists can now take the mock liman
Speaker 2: alpha emission catalogs generated by their simulations and directly cross
Speaker 2: correlate them with the actual spatial distribution observed by eight decks.
Speaker 3: It's the ultimate reality check for theoretical physics.
Speaker 2: If your simulation says the gas should be blown five
Speaker 2: hundred thousand light years away from the galaxy by a quasar,
Speaker 2: but the hetdex map shows the gas is tightly bound
Speaker 2: within one hundred thousand light.
Speaker 3: Years, you know your subgrid physics recipe for a GM
Speaker 3: feedback is wrong.
Speaker 2: It forces the theoretical models to conform to the empirical
Speaker 2: reality exactly and honestly. This dynamic testing sophisticated models against raw, messy,
Speaker 2: real world data is a critical philosophical anchor, not just
Speaker 2: in astrophysics, but in any data driven field. Today. Absolutely,
Speaker 2: we live in an era heavily dependent on predictive models
Speaker 2: and algorithms we simulate climate impacts, economic shifts, epidemiological spread.
Speaker 2: It is incredibly easy to trust the output of a
Speaker 2: model simply because its internal logic is sound.
Speaker 3: But the the X project reminds us that the model
Speaker 3: is merely a hypothesis.
Speaker 2: Until it is aggressively tested against the totality of the
Speaker 2: available data, not just the five percent that is bright,
Speaker 2: clean and easy to measure.
Speaker 3: That is a highly pertinent observation. The reliance on heavily
Speaker 3: filtered data to inform generalized models is a systemic vulnerability
Speaker 3: across all sciences.
Speaker 2: The head dex map unequivocally demonstrates the value of mining
Speaker 2: the discarded data.
Speaker 3: And within the context of astrophysics, this initial map is
Speaker 3: really merely the vanguard.
Speaker 2: Just the beginning.
Speaker 3: The successful application of line intensity mapping to liman alpha
Speaker 3: emission establishes a rigorous methodological foundation for the entire future
Speaker 3: of cosmic.
Speaker 2: Cartography, because the research team is already pivoting toward applying
Speaker 2: this technique to different spectral lines right.
Speaker 3: Yes, to map entirely different components of the galactic ecosystem.
Speaker 2: Because the Liman alpha line only tells us part of
Speaker 2: the story, it traces the ionized and excited neutral hydrogen.
Speaker 3: It shows us where the massive hot stars are irradiating
Speaker 3: the surrounding gas.
Speaker 2: It maps the active, violent regions of the cosmic web.
Speaker 2: But to understand the complete baryon cycle how gas flows
Speaker 2: from the voids, cools, condenses, and eventually form stars, we
Speaker 2: need to map the cold gas as well.
Speaker 3: This raises an important question, how do we observe the
Speaker 3: reservoirs of fuel that haven't ignited yet the dark stuff right?
Speaker 3: The immediate next step outlined by the researchers involves targeting
Speaker 3: the emission lines of carbon monoxide.
Speaker 2: Specifically the rotational transitions of the co molecule.
Speaker 3: Well Linman alpha traces gas at temperatures of roughly ten
Speaker 3: thousand kelvin. Carbon monoxide is an excellent proxy for locating
Speaker 3: giant molecular.
Speaker 2: Clouds where the gas temperatures drop to just tens of
Speaker 2: degrees above absolute.
Speaker 3: Zero, freezing cold.
Speaker 2: And those freezing incredibly dense molecular clouds are the actual
Speaker 2: stellar nurseries. You need cold gas because thermal pressure fights
Speaker 2: against gravity.
Speaker 3: Heat pushes out, gravity pulls in right.
Speaker 2: Only when the gas cools down sufficiently can gravity take over,
Speaker 2: causing the cloud to collapse and ignite nuclear fusion to
Speaker 2: birth new stars.
Speaker 3: Exactly so, by conducting line intensity mapping surveys for carbon
Speaker 3: monoxide using millimeter wavelength rays like LMA or future facilities.
Speaker 2: And cross correlating that data with the HTDX Liman alpha.
Speaker 3: Map, astronomers can effectively trace the entire thermodynamic life cycle
Speaker 3: of galactic evolution.
Speaker 2: You map the cold in falling molecular gas with.
Speaker 3: CO, you map the regions of active massive star formation
Speaker 3: with the bright Liman alpha peaks.
Speaker 2: And you map the diffuse outflowing or heavily irradiated gas
Speaker 2: in the circumgalactic medium with the faint Liman alpha intensity map.
Speaker 3: Layering these maps over the exact same cosmological volume will
Speaker 3: give us a complete multi phase view of the universe
Speaker 3: at its most active epoch.
Speaker 2: We will literally watch the respiratory system of the cosmos.
Speaker 3: Galaxies inhaling cold molecular gas and exhaling hot ionized plasma.
Speaker 2: The technological capacity to do this is rapidly expanding, to
Speaker 2: with pioneering instruments like the hobby Everly Telescope paving the way,
Speaker 2: and upcoming line intensity mapping missions like sphere x and
Speaker 2: exclaim coming online soon.
Speaker 3: We are transitioning from an era of cataloging isolated objects
Speaker 3: to mapping the continuous fluid dynamics of the universe.
Speaker 2: Julian Uinhos correctly categorize this as entering a golden age
Speaker 2: for cosmic mapping.
Speaker 3: It truly is. The HDX publication is a proof of
Speaker 3: concept that fundamentally validates the intensity mapping technique for optical
Speaker 3: and ultraviolet wavelengths on cosmological scales.
Speaker 2: It proves that the faint, unresolved emission is not a
Speaker 2: barrier to observation, but rather a profound source of physical information.
Speaker 3: Let's summarize the sheer scope of what we've discussed today.
Speaker 2: We've ventured ten billion years into the past to the
Speaker 2: cosmic noon, an era defined by extreme galactic assembly.
Speaker 3: We examined how the quantum transition of the hydrogen atom
Speaker 3: produces the one hundred and twenty one point six nanimeter
Speaker 3: liman alpha line, and.
Speaker 2: How cosmological redshift stretches that signal all the way into
Speaker 2: the optical band.
Speaker 3: We detailed the massive engineering of the htd.
Speaker 2: X project, the thirty thousand fivers, and.
Speaker 3: The brilliant statistical pivot from point source cataloging to line
Speaker 3: intensity mapping.
Speaker 2: By utilizing supercomputers to cross correlate the known coordinates of
Speaker 2: one million bright galaxies With half a petabyte of highly smooth,
Speaker 2: previously discarded data, the team successfully illuminated the faint, diffuse
Speaker 2: filaments of the cosmic web.
Speaker 3: They transformed ninety five percent background noise into the most
Speaker 3: robust empirical test of cosmological simulations ever created.
Speaker 2: The transition from viewing the universe as a collection of
Speaker 2: discrete luminous points to a continuous, interconnected topological field. It's
Speaker 2: just an extraordinary scientific advancement.
Speaker 3: It underscores a fundamental principle. Really, the limits of our
Speaker 3: understanding are often dictated not by the absence of information,
Speaker 3: but by our methodological filters. Wow, we structured our most
Speaker 3: advanced cosmological models while ignoring ninety five percent of the
Speaker 3: observational data because it failed to cross an arbitrary threshold
Speaker 3: of clarity.
Speaker 2: Which demands that we ask a pretty profound question.
Speaker 3: It does if the literal, connective tissue of the universe
Speaker 3: was hiding in the noise we systematically discarded. What other
Speaker 3: foundational truths in physics, in the complex systems of our
Speaker 3: own planet, or even the structure of our societies are
Speaker 3: we completely missing simply because we have not yet developed
Speaker 3: the framework to find meaning in the blur.
Speaker 2: The h x map proves the information is there. The
Speaker 2: challenge is having the vision to read.
Speaker 4: It tis the nations choo