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DOC: EHT Case Study Suggestions #196

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DOC: Re-work EHT intro paragraph
rossbar Mar 22, 2020
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Updates to Key Imaging Objectives
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Modified the wording of a couple bullet points.
rossbar Mar 23, 2020
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Re-wrote first paragraph of NumPy's role section
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148 changes: 74 additions & 74 deletions content/en/case-studies/blackhole-image.md
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Expand Up @@ -10,39 +10,43 @@ sidebar: false
<footer align="right">—Katie Bouman, <cite>Assistant Professor, Computing & Mathematical Sciences, Caltech</cite></footer>
</blockquote>

## About Event Horizon Telescope
## About The Event Horizon Telescope

The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org), is an
array of eight ground-based radio telescopes forming a computational telescope
the size of the earth, that are designed to study extreme objects in the
universe, with unprecedented sensitivity and resolution. It is a worldwide
network of eight pre-existing telescopes based on a technique called
very-long-baseline interferometry (VLBI). This technique is used to
synchronize these telescopes deployed across the globe to form one huge,
Earth-size telescope capable of observing at a wavelength of 1.3 mm. This
means EHT can achieve an angular resolution of
[20 micro-arcseconds](https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole) —
enough to read a newspaper in New York from a sidewalk café in Paris!

### Key Imaging Objectives

* To study the most extreme objects in the Universe predicted by Einstein’s
theory of general relativity, during the centennial year of the historic
experiment that first confirmed the theory (2017).

* Focus on the [black hole](https://solarsystem.nasa.gov/resources/2319/first-image-of-a-black-hole/)
at the center of Messier 87 galaxy, located in the Virgo galaxy cluster.
the size of the earth, designed to study extreme objects in the
universe with unprecedented sensitivity and resolution. The worldwide
network of radio telescopes comprises a virtual telescope based on a technique
called very-long-baseline interferometry (VLBI).
Using this technique, the EHT is able to achieve an angular resolution of
[20 micro-arcseconds][resolution] — enough to read a newspaper in New York
from a sidewalk café in Paris!

[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole

### Key Goals and Results

* The EHT is an exciting new tool for studying the most extreme objects in the
universe. The EHT's groundbreaking image was published 100 years
after [Sir Arthur Eddington's expidition][eddington] yielded the first
observational evidence in support of Einstein's theory of general relativity.

* The EHT's first image focuses on the supermassive black hole at the center
of the galaxy Messier 87 (M87), located in the Virgo galaxy cluster.
This black hole resides approximately 55 million light-years from Earth and
has a mass equal to 6.5 billion times that of the Sun. It has been a
subject of astronomical study for
[over a 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385).
Black holes have been theoretically predicted and observed but a real image
was never created until now.
Black holes have long been the object of intense study but the EHT provides
the first direct visual evidence of these extreme phenomena.

* Based on Einstein’s general theory of relativity, the scientists expected to
* Based on Einstein’s general theory of relativity, scientists expected
see a dark region similar to a shadow, caused by the gravitational bending
and capture of light by the event horizon. By studying this shadow
scientists could measure the enormous mass of M87’s black hole.
scientists could measure the enormous mass of M87’s central supermassive
black hole.

[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment

### The Challenges

Expand All @@ -60,75 +64,71 @@ enough to read a newspaper in New York from a sidewalk café in Paris!
Terabytes worth of observed data per day, stored on high-performance
helium filled hard drives.

* **Source Imaging and Model Fitting**
* **Image Reconstruction**

The sequence of correlation and engineering releases represents a
year-long effort of identifying and mitigating data issues, and developing
new software and procedures that could reliably choose the most likely
image based on actual measurements.
How are the calibrated data processed to produce an image of something that
has never before been directly imaged? How can scientists be confident
that the image is correct? These are some of the challenges overcome in
the analysis to produce the image.

{{< figure src="/images/content_images/cs/dataprocessbh.png" class="csfigcaption" caption="**EHT Data Processing Pipeline**" alt="data pipeline" align="middle" attr="(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)" attrlink="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57" >}}

## NumPy’s Role in Black Hole Imaging

There are several aspects to black hole imaging besides data collection, noise
elimination, data cleanup, reduction and correlation. Imaging is crucial as it
can help to predict not only the black hole mass but also rule out whether a
black hole could be a wormhole, a theoretical bridge between distant points
in spacetime. But it is also incredibly hard to measure given the astronomical
distances involved. As Katie Bouman mentions in her
[TED talk](https://www.youtube.com/watch?v=BIvezCVcsYs),
‘It is like taking a picture of an orange on the surface of the moon.’
While collecting, curating, and processing the data from the EHT facilities
represents a monumental challenge, it is only the first step in generating
an image from the data.
There are many approaches to image reconstruction, each incorporating unique
assumptions and constraints in order to solve the ill-posed problem of
recovering an image of the black hole from the collected data.
But how can anyone be confident that the image that's produced is correct?
What if there's a problem with the data? Or perhaps an algorithm relies too
heavily on a particular assumption? Will the image change drastically if a
single parameter is changed?
The EHT collaboration met these challenges by having independent teams
evaluate the data using both established and cutting-edge image reconstruction
techniques to verify that the resulting images were consistent.
Results from these independent teams of researchers were combined to yield the
first-of-a-kind image of the black hole.
This approach is a powerful example of the importance of reproducibility and
collaboration to modern scientific discovery, and illustrates the role that
the scientific Python ecosystem plays in supporting scientific advancement
through collaborative data analysis.

{{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="role of numpy" caption="**The role of NumPy in Black Hole imaging**" >}}

Once the key challenges posed by EHT, data collection and reduction are taken
care of, the next big challenge in data processing is related to imaging. The
imaging algorithms form the core of this task as through imaging, scientists
could calculate the shadow of the black hole which forms the crux of several
other calculations related to event horizon and nearby objects. One of the key
algorithms used in imaging was developed by Katie Bouman – Continuous
High-resolution Image Reconstruction using Patch priors, or ‘CHIRP’. It can
parse the cumulative telescope data gathered by the Event Horizon Telescope
project.For imaging tasks, researchers banked on Python to run the datasets on
these algorithms, arraying and plotting data for meaningful insights.

Besides NumPy, there were other packages such as
[SciPy](https://www.scipy.org), [Pandas](https://pandas.io) and
[Matplotlib](https://matplotlib.org) that helped in imaging and
[data processing for imaging the black hole](https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57).
The standard astronomical file formats and time/coordinate transformations
were handled by [Astropy](https://www.astropy.org) while Matplotlib was used
in visualizing data throughout the analysis pipeline, including the generation
of the final image of the black hole.

Andrew Chael and the ehtim project team came up with
[eht-imaging](https://github.com/achael/eht-imaging) Python modules for
simulating and manipulating VLBI data and producing images with regularized
maximum likelihood methods. NumPy is at the core of array data processing used
in this software package named ehtim as indicated by the partial software
For example, the [`eht-imaging`][ehtim] Python package provides tools for
simulating and performing image reconstruction on VLBI data.
NumPy is at the core of array data processing used
in this package as illustrated by the partial software
dependency chart below.

{{< figure src="/images/content_images/cs/ehtim_numpy.png" class="fig-center" alt="ehtim dependency map highlighting numpy" caption="**Software dependency chart of ehtim package highlighting NumPy**" >}}

The challenge posed during reconstruction of an image using VLBI measurements
is that there can be an infinite number of possible images that explain the
data. The ehtim software addresses this challenge by implementing algorithms
that help find a set of most likely reasonable images that respects prior
scientific assumptions while still satisfying the observed data.
[ehtim]: https://github.com/achael/eht-imaging

Besides NumPy, many other packages such as
[SciPy](https://www.scipy.org) and [Pandas](https://pandas.io) were used in the
data processing pipeline for imaging the black hole.
The standard astronomical file formats and time/coordinate transformations
were handled by [Astropy][astropy] while [Matplotlib][mpl] was used
in visualizing data throughout the analysis pipeline, including the generation
of the final image of the black hole.

[astropy]: https://www.astropy.org/
[mpl]: https://matplotlib.org/

## Summary

NumPy enabled researchers to manipulate large numerical datasets through its
efficient data structures of vectors and matrices leading to imaging and
plotting of the first ever image of a black hole. Imaging of M87 black hole is
a major scientific feat that was almost presumed impossible a century ago. In
a way, black hole image has helped in a stunning confirmation of Einstein’s
general theory of relativity. This is not only a breakthrough in technology,
but an example of international scale collaboration that uses connections
between the world's best radio observatories, over 200 scientists worked with
observations collected over 10 days and analyzed for over a year. They used
efficient and generic n-dimensional array, providing a foundation for the
software used to generated the first ever image of
a black hole. The direct imaging of a black hole is
a major scientific accomplishment providing stunning, visual evidence of Einstein’s
general theory of relativity. This achievement encompasses not only
technological breakthroughs, but international-scale scientific collaboration
between over 200 scientists and some of the world's best radio observatories.
They used
innovative algorithms and data processing techniques improving upon existing
astronomical models to help unfold some of the mysteries of the universe.

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