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In the movie "Interstellar,"
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we get an up-close look
at a supermassive black hole.
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Set against a backdrop of bright gas,
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the black hole's massive
gravitational pull
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bends light into a ring.
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However, this isn't a real photograph,
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but a computer graphic rendering --
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an artistic interpretation
of what a black hole might look like.
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A hundred years ago,
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Albert Einstein first published
his theory of general relativity.
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In the years since then,
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scientists have provided
a lot of evidence in support of it.
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But one thing predicted
from this theory, black holes,
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still have not been directly observed.
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Although we have some idea
as to what a black hole might look like,
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we've never actually taken
a picture of one before.
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However, you might be surprised to know
that that may soon change.
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We may be seeing our first picture
of a black hole in the next couple years.
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Getting this first picture will come down
to an international team of scientists,
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an Earth-sized telescope
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and an algorithm that puts together
the final picture.
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Although I won't be able to show you
a real picture of a black hole today,
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I'd like to give you a brief glimpse
into the effort involved
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in getting that first picture.
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My name is Katie Bouman,
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and I'm a PhD student at MIT.
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I do research in a computer science lab
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that works on making computers
see through images and video.
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But although I'm not an astronomer,
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today I'd like to show you
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how I've been able to contribute
to this exciting project.
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If you go out past
the bright city lights tonight,
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you may just be lucky enough
to see a stunning view
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of the Milky Way Galaxy.
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And if you could zoom past
millions of stars,
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26,000 light-years toward the heart
of the spiraling Milky Way,
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we'd eventually reach
a cluster of stars right at the center.
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Peering past all the galactic dust
with infrared telescopes,
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astronomers have watched these stars
for over 16 years.
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But it's what they don't see
that is the most spectacular.
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These stars seem to orbit
an invisible object.
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By tracking the paths of these stars,
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astronomers have concluded
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that the only thing small and heavy
enough to cause this motion
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is a supermassive black hole --
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an object so dense that it sucks up
anything that ventures too close --
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even light.
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But what happens if we were
to zoom in even further?
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Is it possible to see something
that, by definition, is impossible to see?
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Well, it turns out that if we were
to zoom in at radio wavelengths,
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we'd expect to see a ring of light
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caused by the gravitational
lensing of hot plasma
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zipping around the black hole.
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In other words,
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the black hole casts a shadow
on this backdrop of bright material,
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carving out a sphere of darkness.
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This bright ring reveals
the black hole's event horizon,
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where the gravitational pull
becomes so great
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that not even light can escape.
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Einstein's equations predict
the size and shape of this ring,
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so taking a picture of it
wouldn't only be really cool,
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it would also help to verify
that these equations hold
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in the extreme conditions
around the black hole.
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However, this black hole
is so far away from us,
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that from Earth, this ring appears
incredibly small --
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the same size to us as an orange
on the surface of the moon.
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That makes taking a picture of it
extremely difficult.
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Why is that?
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Well, it all comes down
to a simple equation.
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Due to a phenomenon called diffraction,
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there are fundamental limits
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to the smallest objects
that we can possibly see.
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This governing equation says
that in order to see smaller and smaller,
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we need to make our telescope
bigger and bigger.
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But even with the most powerful
optical telescopes here on Earth,
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we can't even get close
to the resolution necessary
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to image on the surface of the moon.
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In fact, here I show one of the highest
resolution images ever taken
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of the moon from Earth.
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It contains roughly 13,000 pixels,
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and yet each pixel would contain
over 1.5 million oranges.
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So how big of a telescope do we need
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in order to see an orange
on the surface of the moon
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and, by extension, our black hole?
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Well, it turns out
that by crunching the numbers,
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you can easily calculate
that we would need a telescope
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the size of the entire Earth.
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(Laughter)
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If we could build
this Earth-sized telescope,
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we could just start to make out
that distinctive ring of light
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indicative of the black
hole's event horizon.
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Although this picture wouldn't contain
all the detail we see
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in computer graphic renderings,
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it would allow us to safely get
our first glimpse
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of the immediate environment
around a black hole.
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However, as you can imagine,
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building a single-dish telescope
the size of the Earth is impossible.
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But in the famous words of Mick Jagger,
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"You can't always get what you want,
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but if you try sometimes,
you just might find
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you get what you need."
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And by connecting telescopes
from around the world,
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an international collaboration
called the Event Horizon Telescope
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is creating a computational telescope
the size of the Earth,
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capable of resolving structure
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on the scale of a black
hole's event horizon.
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This network of telescopes is scheduled
to take its very first picture
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of a black hole next year.
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Each telescope in the worldwide
network works together.
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Linked through the precise timing
of atomic clocks,
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teams of researchers at each
of the sights freeze light
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by collecting thousands
of terabytes of data.
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This data is then processed in a lab
right here in Massachusetts.
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So how does this even work?
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Remember if we want to see the black hole
in the center of our galaxy,
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we need to build this impossibly large
Earth-sized telescope?
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For just a second,
let's pretend we could build
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a telescope the size of the Earth.
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This would be a little bit
like turning the Earth
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into a giant spinning disco ball.
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Each individual mirror would collect light
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that we could then combine
together to make a picture.
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However, now let's say
we remove most of those mirrors
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so only a few remained.
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We could still try to combine
this information together,
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but now there are a lot of holes.
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These remaining mirrors represent
the locations where we have telescopes.
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This is an incredibly small number
of measurements to make a picture from.
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But although we only collect light
at a few telescope locations,
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as the Earth rotates, we get to see
other new measurements.
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In other words, as the disco ball spins,
those mirrors change locations
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and we get to observe
different parts of the image.
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The imaging algorithms we develop
fill in the missing gaps of the disco ball
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in order to reconstruct
the underlying black hole image.
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If we had telescopes located
everywhere on the globe --
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in other words, the entire disco ball --
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this would be trivial.
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However, we only see a few samples,
and for that reason,
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there are an infinite number
of possible images
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that are perfectly consistent
with our telescope measurements.
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However, not all images are created equal.
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Some of those images look more like
what we think of as images than others.
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And so, my role in helping to take
the first image of a black hole
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is to design algorithms that find
the most reasonable image
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that also fits the telescope measurements.
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Just as a forensic sketch artist
uses limited descriptions
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to piece together a picture using
their knowledge of face structure,
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the imaging algorithms I develop
use our limited telescope data
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to guide us to a picture that also
looks like stuff in our universe.
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Using these algorithms,
we're able to piece together pictures
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from this sparse, noisy data.
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So here I show a sample reconstruction
done using simulated data,
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when we pretend to point our telescopes
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to the black hole
in the center of our galaxy.
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Although this is just a simulation,
reconstruction such as this give us hope
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that we'll soon be able to reliably take
the first image of a black hole
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and from it, determine
the size of its ring.
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Although I'd love to go on
about all the details of this algorithm,
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luckily for you, I don't have the time.
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But I'd still like
to give you a brief idea
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of how we define
what our universe looks like,
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and how we use this to reconstruct
and verify our results.
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Since there are an infinite number
of possible images
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that perfectly explain
our telescope measurements,
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we have to choose
between them in some way.
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We do this by ranking the images
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based upon how likely they are
to be the black hole image,
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and then choosing the one
that's most likely.
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So what do I mean by this exactly?
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Let's say we were trying to make a model
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that told us how likely an image
were to appear on Facebook.
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We'd probably want the model to say
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it's pretty unlikely that someone
would post this noise image on the left,
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and pretty likely that someone
would post a selfie
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like this one on the right.
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The image in the middle is blurry,
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so even though it's more likely
we'd see it on Facebook
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compared to the noise image,
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it's probably less likely we'd see it
compared to the selfie.
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But when it comes to images
from the black hole,
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we're posed with a real conundrum:
we've never seen a black hole before.
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In that case, what is a likely
black hole image,
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and what should we assume
about the structure of black holes?
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We could try to use images
from simulations we've done,
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like the image of the black hole
from "Interstellar,"
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but if we did this,
it could cause some serious problems.
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What would happen
if Einstein's theories didn't hold?
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We'd still want to reconstruct
an accurate picture of what was going on.
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If we bake Einstein's equations
too much into our algorithms,
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we'll just end up seeing
what we expect to see.
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In other words,
we want to leave the option open
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for there being a giant elephant
at the center of our galaxy.
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(Laughter)
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Different types of images have
very distinct features.
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We can easily tell the difference
between black hole simulation images
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and images we take
every day here on Earth.
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We need a way to tell our algorithms
what images look like
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without imposing one type
of image's features too much.
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One way we can try to get around this
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is by imposing the features
of different kinds of images
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and seeing how the type of image we assume
affects our reconstructions.
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If all images' types produce
a very similar-looking image,
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then we can start to become more confident
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that the image assumptions we're making
are not biasing this picture that much.
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This is a little bit like
giving the same description
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to three different sketch artists
from all around the world.
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If they all produce
a very similar-looking face,
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then we can start to become confident
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that they're not imposing their own
cultural biases on the drawings.
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One way we can try to impose
different image features
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is by using pieces of existing images.
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So we take a large collection of images,
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and we break them down
into their little image patches.
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We then can treat each image patch
a little bit like pieces of a puzzle.
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And we use commonly seen puzzle pieces
to piece together an image
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that also fits our telescope measurements.
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Different types of images have
very distinctive sets of puzzle pieces.
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So what happens when we take the same data
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but we use different sets of puzzle pieces
to reconstruct the image?
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Let's first start with black hole
image simulation puzzle pieces.
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OK, this looks reasonable.
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This looks like what we expect
a black hole to look like.
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But did we just get it
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because we just fed it little pieces
of black hole simulation images?
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Let's try another set of puzzle pieces
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from astronomical, non-black hole objects.
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OK, we get a similar-looking image.
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And then how about pieces
from everyday images,
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like the images you take
with your own personal camera?
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Great, we see the same image.
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When we get the same image
from all different sets of puzzle pieces,
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then we can start to become more confident
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that the image assumptions we're making
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aren't biasing the final
image we get too much.
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Another thing we can do is take
the same set of puzzle pieces,
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such as the ones derived
from everyday images,
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and use them to reconstruct
many different kinds of source images.
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So in our simulations,
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we pretend a black hole looks like
astronomical non-black hole objects,
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as well as everyday images like
the elephant in the center of our galaxy.
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When the results of our algorithms
on the bottom look very similar
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to the simulation's truth image on top,
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then we can start to become
more confident in our algorithms.
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And I really want to emphasize here
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that all of these pictures were created
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by piecing together little pieces
of everyday photographs,
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like you'd take with your own
personal camera.
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So an image of a black hole
we've never seen before
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may eventually be created by piecing
together pictures we see all the time
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of people, buildings,
trees, cats and dogs.
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Imaging ideas like this
will make it possible for us
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to take our very first pictures
of a black hole,
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and hopefully, verify
those famous theories
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on which scientists rely on a daily basis.
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But of course, getting
imaging ideas like this working
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would never have been possible
without the amazing team of researchers
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that I have the privilege to work with.
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It still amazes me
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that although I began this project
with no background in astrophysics,
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what we have achieved
through this unique collaboration
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could result in the very first
images of a black hole.
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But big projects like
the Event Horizon Telescope
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are successful due to all
the interdisciplinary expertise
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different people bring to the table.
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We're a melting pot of astronomers,
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physicists, mathematicians and engineers.
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This is what will make it soon possible
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to achieve something
once thought impossible.
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I'd like to encourage all of you to go out
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and help push the boundaries of science,
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even if it may at first seem
as mysterious to you as a black hole.
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Thank you.
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(Applause)