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In 2019, the Event Horizon Telescope gave humanity its first direct glimpse of a black
hole.
It was the supermassive black hole lurking at the heart of the galaxy M87, surrounded
by a ring of radio light, narrowly escaping the black hole's gravity.
But since then, four members of the original Event Horizon Telescope team developed a new
machine learning algorithm to create an all new image of M87’s black hole.
This is not an enhancement of the original 2019 image, but rather it's an all new reconstruction
of the data made with a new imaging algorithm created by Dr. Lia Medeiros and her collaborators.
The results were published in the Astrophysical Journal Letters.
The result is stunning, and the ring is now at least a factor of two thinner than the
original image.
The new image shows just how narrow the ring of light is relative to the black hole's shadow.
We talked in detail about how a black hole should appear to our eyes in a previous video.
But to quickly summarize, the black hole deflects light rays into a ring of photons as seen
on the sky.
So the dark area really isn't the black hole's event horizon, but rather is the shadow the
black hole casts.
Because M87 is 55 million light years from Earth, the black hole's shadow is so small
in the sky that you need a telescope the size of Earth to detect it.
But you can't cover the entire planet in telescopes.
I should know.
I tried and they stopped me.
So the best the EHT collaboration could do at the time was to just use seven radio telescopes
at five locations.
Those telescopes were able to directly capture a portion of the black hole's image.
The rest would have to be reconstructed from the available data.
Reconstructions aren't perfect, and there's always going to be some uncertainty as to
where the information lies within the image.
And that's part of the reason why, despite being the highest resolution image ever taken,
it was still a little blurry.
Maximizing the level of detail in the image is critical for understanding more about the
black hole's behavior.
So Medeiros and her team created a new image reconstruction algorithm called Principal
Component Interferometric Modeling, or PRIMO.
The result is an image that's far closer to the actual resolution of the Event Horizon
Telescope array.
Obviously, the main difference in the new image is that the ring is a lot thinner.
In fact, it's at most half the thickness of the original image.
In theory, the ring would be extremely thin and bright, but the fact that it's still a
little blurry is partly an artifact of the new reconstruction.
And partly because a blurring filter was used to match the reconstructed image’s resolution
to that of the rest of the Event Horizon Telescope's.
Otherwise, the PRIMO image is largely consistent with the original reconstruction.
Both images show a ring that's slightly brighter in the south than in the north.
The reason for the difference in brightness is because the black hole is rotating clockwise
on the plane of the sky, but it's tilted slightly away from us by about 17 degrees.
The surrounding disc is tilted as well and launches the jet that emerges from M87.
Now, because of the sideways tilt, the southern half of the ring is rotating toward us ever
so slightly, but at nearly the speed of light.
So this lower part of the disk gets Doppler boosted and appears brighter.
The northern half, however, is rotating slightly away from us, so the effect is reversed and
the top part appears dimmer.
Both images are consistent in terms of the size, shape and brightness of the ring.
So that means that the new PRIMO image is probably more representative of what M87’s
black hole actually looks like at EHT’s resolution and wavelength.
But how did PRIMO know how to reconstruct this new image?
And why is it so much sharper than the original?
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Now you might wonder why the EHT team didn't just skip the original “thick donut” image
and start using PRIMO to reconstruct the image in the first place?
Well, it turns out that PRIMO couldn't exist without the original work done by the collaboration.
You see, what makes creating images of black holes like this so challenging is that you
have these very sparse data sets to work with.
Each pair of radio telescopes captures just a tiny piece of the image, so the rest of
the image has to be reconstructed from that limited data.
When the original image was being processed, the team needed to be certain that the image
was a true reconstruction from the data and not one that was biased by any assumptions
of how black holes should appear.
That's why the team split up into groups and used different algorithms, each with their
own set of priorities, assumptions and choices.
Those algorithms created images that matched the EHT data without assuming how the black
hole should appear.
That means the algorithms could have made lots of different shapes, not just rings.
As a matter of fact, the images didn't even have to conform to predictions made by general
relativity.
In other words, the algorithms were model-agnostic.
But because the algorithms were so agnostic of the structure, the uncertainty in that
reconstruction manifested as the broad fuzzy donut of a ring.
However, because the original work showed that the structure had to be a ring formed
by general relativity, PRIMO could approach its reconstruction with this understanding
in mind, and that allowed PRIMO to infer the maximum amount of information from the data.
Still, Primo had to be trained on what black holes look like in general.
To that end, PRIMO relies on a type of artificial intelligence called dictionary learning.
This is a branch of machine learning which enables computers to generate rules based
on large sets of training data.
For example, if a computer were trained on a set of banana images, it should be able
to reconstruct an image of another banana from an incomplete image.
In the case of M87, PRIMO was trained on a set of 30,000 high resolution general relativistic
magnetohydrodynamic simulations, each one based on the equations of general relativity,
thermodynamics, because of the extremely hot gases moving about, plasma physics, because
the gas is ionized, and electromagnetism, because the moving plasma generates a magnetic
field.
Even then, there were a wide variety of parameters, such as different orientations, masses, spins,
disk temperatures and other factors.
In the end, the library rounded out to about 30,000 of these simulations.
That's a lot of data.
But PRIMO uses another trick.
The “P” in PRIMO stands for Principal Component Analysis, or PCI.
It's a technique for analyzing large datasets containing a high number of different combinations
of parameters.
It's really good at figuring out the patterns in the data and working out what's similar
and what's different.
Thus, PCA acts as a kind of sonic screwdriver for understanding and analyzing an otherwise
overwhelmingly complex dataset.
Armed with its 30,000 black hole dictionary and its PCA screwdriver, PRIMO set about reconstructing
images from the original 2017 data.
The result was a set of images featuring a ring consistent with both the data and with
the original EHT results as well.
But now there are some new differences in the PRIMO image.
For example, the bright region in the lower left portion of the 2019 image is now much
dimmer in the PRIMO version.
And there's also what appears to be a tail like structure emerging from the bottom of
the primo ring, and that makes the image look a little bit more like a “Q” than an “O”.
Even more interesting is how the ring appears to change slightly over time.
M87’s black hole is huge and its event horizon spans about a light-week across.
As such, any phenomenon near the horizon of the black hole should take about a week or
so to play out.
And that appears to be represented in the PRIMO images created from the 2017 observing
run.
However, knowing what to make of these new features is going to take a lot more analysis.
These could represent physical phenomena, or maybe they're just artifacts from the reconstruction,
the work of interpreting the physical information represented in the new images are underway
now, including a refined estimate of M87’s mass.
For now, though, this paper is showing the results of applying the PRIMO Reconstruction
technique, which I think is pretty safe to say is amazing.
And of course, the team will eventually apply PRIMO to the dataset for Sagittarius A-star
as well.
But it makes sense that they want to make sure they really understand the information
in the new image of M87 because Sagittarius A-star is a lot smaller.
That makes it a much more dynamic and active black hole, which in turn makes reconstructing
its image a lot more difficult.
But what I really think is cool is that even this new image is still just a tease of what's
to come.
The 2017 dataset had seven telescopes from five locations observing M87, but three new
telescopes have been added to the array since then.
More telescopes means more image data can be made available, and less of that image
will have to be reconstructed.
Oh yeah.
And they're also observing M87 and Sagittarius A-star again as of this filming.
Then there's also the next generation EHT, which plans to go to shorter wavelengths to
get even higher resolution.
The team would like to be able to create movies of these black holes as well, and even color
images by imaging at multiple radio wavelengths.
Having tools like PRIMO means the results will be that much closer to reality.
My thanks as always to my patrons for helping to keep Launch Pad Astronomy going.
And I'd like to welcome Richard Pearce as my newest galactic level supporter.
Thank you, Richard.
And if you'd like to join me on this journey to this incredible universe of ours, well,
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Until next time, stay curious, my friend.