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* DOC: Re-work EHT intro paragraph
* Removed sentence about EHT sensing wavelength.
* Updated wording surrounding VLBI
* Switch to md reflink style for source readability
* Updates to Key Imaging Objectives
* Renamed the section to 'Key Goals and Results' to better fit the content.
* Updated first bullet point with context and accurate date.
* Minor re-wording of remaining bullet points
* Modified the wording of a couple bullet points.
* Made the reference to M87's black hole more explicit
* Updated last bullet point of Challenges to give a higher-level overview
of the imaging challeng.
* Re-wrote first paragraph of NumPy's role section
* Attempting to focus specifically on image reconstruction,
particularly eht-imaging, where the dependence on the scientific
Python ecosystem is most relevant.
* Trying to provide a little background on overall reconstruction
task, and tie it into why NumPy/SciPy are so important.
* More reorganization of the NumPy Role section.
* Moved eht-imaging up as it ties in with the theme and is the
most prominent example of NumPy used in the eht analysis
* Removed paragraph on CHIRP:
- Trying to reduced redundancy given previous discussion on data
processing
- CHIRP is not available on GH - not sure to what extent NumPy is
used for that particular pkg
* Removed additional paragraph on regularized MLEM - want to avoid
too much detail and focusing on algorithms
* Re-wording of the summary.
* replaced vectors/matrices with n-dimensional array
* Emphasize importance of discovery and role of collaboration.
* Final pass through for wording/grammatical edits.
* Added bolded titles to bulleted list in eht casestudy.
Addresses comments from review.
‘It is like taking a picture of an orange on the surface of the moon.’
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While collecting, curating, and processing the data from the EHT facilities
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represents a monumental challenge, it is only the first step in generating
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an image from the data.
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There are many approaches to image reconstruction, each incorporating unique
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assumptions and constraints in order to solve the ill-posed problem of
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recovering an image of the black hole from the collected data.
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But how can anyone be confident that the image that's produced is correct?
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What if there's a problem with the data? Or perhaps an algorithm relies too
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heavily on a particular assumption? Will the image change drastically if a
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single parameter is changed?
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The EHT collaboration met these challenges by having independent teams
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evaluate the data using both established and cutting-edge image reconstruction
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techniques to verify that the resulting images were consistent.
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Results from these independent teams of researchers were combined to yield the
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first-of-a-kind image of the black hole.
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This approach is a powerful example of the importance of reproducibility and
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collaboration to modern scientific discovery, and illustrates the role that
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the scientific Python ecosystem plays in supporting scientific advancement
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through collaborative data analysis.
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{{< 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**" >}}
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Once the key challenges posed by EHT, data collection and reduction are taken
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care of, the next big challenge in data processing is related to imaging. The
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imaging algorithms form the core of this task as through imaging, scientists
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could calculate the shadow of the black hole which forms the crux of several
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other calculations related to event horizon and nearby objects. One of the key
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algorithms used in imaging was developed by Katie Bouman – Continuous
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High-resolution Image Reconstruction using Patch priors, or ‘CHIRP’. It can
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parse the cumulative telescope data gathered by the Event Horizon Telescope
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project.For imaging tasks, researchers banked on Python to run the datasets on
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these algorithms, arraying and plotting data for meaningful insights.
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Besides NumPy, there were other packages such as
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[SciPy](https://www.scipy.org), [Pandas](https://pandas.io) and
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[Matplotlib](https://matplotlib.org) that helped in imaging and
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[data processing for imaging the black hole](https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57).
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The standard astronomical file formats and time/coordinate transformations
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were handled by [Astropy](https://www.astropy.org) while Matplotlib was used
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in visualizing data throughout the analysis pipeline, including the generation
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of the final image of the black hole.
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Andrew Chael and the ehtim project team came up with
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[eht-imaging](https://github.com/achael/eht-imaging) Python modules for
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simulating and manipulating VLBI data and producing images with regularized
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maximum likelihood methods. NumPy is at the core of array data processing used
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in this software package named ehtim as indicated by the partial software
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For example, the [`eht-imaging`][ehtim] Python package provides tools for
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simulating and performing image reconstruction on VLBI data.
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NumPy is at the core of array data processing used
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in this package as illustrated by the partial software
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