Deploying to main from @ numpy/numpy.org@70a27e7 🚀

Author: InessaPawson

case-studies/gw-discov/index.html

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 effects on many areas of physics including gravitation, relativity,
 astrophysics, cosmology, particle physics, and nuclear physics.</li><li>Crunch observed data via numerical relativity computations that involves
 complex maths in order to discern signal from noise, filter out relevant
-signal and statistically estimate significance of observed data</li><li>Data visualization so that the binary / numerical results can be
+signal and statistically estimate significance of observed data.</li><li>Data visualization so that the binary / numerical results can be
 comprehended.</li></ul><h3 id=the-challenges>The Challenges<a class=headerlink href=#the-challenges title="Link to this heading">#</a></h3><ul><li><p><strong>Computation</strong></p><p>Gravitational Waves are hard to detect as they produce a very small effect
 and have tiny interaction with matter. Processing and analyzing all of
 LIGO&rsquo;s data requires a vast computing infrastructure.After taking care of
 noise, which is billions of times of the signal, there is still very
 complex relativity equations and huge amounts of data which present a
 computational challenge:
 <a href=https://youtu.be/7mcHknWWzNI>O(10^7) CPU hrs needed for binary merger analyses</a>
-spread on 6 dedicated LIGO clusters</p></li><li><p><strong>Data Deluge</strong></p><p>As observational devices become more sensitive and reliable, the challenges
+spread on 6 dedicated LIGO clusters.</p></li><li><p><strong>Data Deluge</strong></p><p>As observational devices become more sensitive and reliable, the challenges
 posed by data deluge and finding a needle in a haystack rise multi-fold.
 LIGO generates terabytes of data every day! Making sense of this data
 requires an enormous effort for each and every detection. For example, the
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 contains a signal - needle in a haystack</li><li>Statistical analysis: estimate the statistical significance of observational
 data, estimating the signal parameters (e.g. masses of stars, spin velocity,
 and distance) by comparison with a model.</li><li>Visualization of data<ul><li>Time series</li><li>Spectrograms</li></ul></li><li>Compute Correlations</li><li>Key <a href=https://github.com/lscsoft>Software</a> developed in GW data analysis
-such as <a href=https://gwpy.github.io/docs/stable/overview.html>GwPy</a> and
+such as <a href=https://gwpy.github.io/docs/stable/overview/>GwPy</a> and
 <a href=https://pycbc.org>PyCBC</a> uses NumPy and AstroPy under the hood for
 providing object based interfaces to utilities, tools, and methods for
 studying data from gravitational-wave detectors.</li></ul><figure class=align-default id=id003><img src=/images/content_images/cs/gwpy-numpy-dep-graph.png alt="gwpy-numpy depgraph" class=align-center><figcaption><strong class=caption-title>Dependency graph showing how GwPy package depends on NumPy</strong><a class=headerlink href=#id003 title="Link to this image">#</a><br><p><span class=caption-text></span></figcaption></figure><hr><figure class=align-default id=id004><img src=/images/content_images/cs/PyCBC-numpy-dep-graph.png alt="PyCBC-numpy depgraph" class=align-center><figcaption><strong class=caption-title>Dependency graph showing how PyCBC package depends on NumPy</strong><a class=headerlink href=#id004 title="Link to this image">#</a><br><p><span class=caption-text></span></figcaption></figure><h2 id=summary>Summary<a class=headerlink href=#summary title="Link to this heading">#</a></h2><p>GW detection has enabled researchers to discover entirely unexpected phenomena