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 ********
 Overview
 ********
+
+----------------------------------
+What is Astrophysical Flavor data?
+----------------------------------
+
+This is data of the *flavor* of a neutrino taken at the `IceCube neutrino
+observatory <https://icecube.wisc.edu/>`_, which is a cubic kilometer array of
+optical sensors embedded in the glacial ice at the South Pole. In particular,
+*astrophysical* neutrinos are ones that are very-high-energy and come from
+astrophysical origins such as `active galactic nuclei
+<https://doi.org/10.1126/science.aat2890>`_. For more on the physics behind
+neutrinos see the :doc:`physics` section.
+
+-------------------------------------
+What does the GolemFlavor package do?
+-------------------------------------
+
+This package provides utilities for astrophysical neutrino propagation and
+Bayesian statistical modeling focused on advanced Markov Chain Monte Carlo
+(MCMC) algorithms. It has been used to make constraints on New Physics models
+in the Astrophysical Flavor, as motivated by the paper `*New Physics in
+Astrophysical Neutrino Flavor*
+<https://doi.org/10.1103/PhysRevLett.115.161303>`_.  For more information on
+the statistical modeling see the :doc:`statistics` section.
+
+--------
+Features
+--------
+
+- **Portable Flavor Functions**: A set of useful functions for calculating measured flavor compositions given a source composition and a mixing matrix.
+- **MCMC Algorithms**: Affine invariant and nested sampling algorithms provided by `emcee <https://emcee.readthedocs.io/>`_ and `MultiNest <https://doi.org/10.1111/j.1365-2966.2009.14548.x>`_.
+- **Anarchic Sampling**: Sampling of the neutrino mixing matrix is done under the `*neutrino mixing anarchy* <https://doi.org/10.1016/j.physletb.2003.08.045>`_ hypothesis to ensure an unbiased prior.
+- **Distributed and parallel computing**: Scripts included to manage the workload across a CPU cluster using `HTCondor <https://research.cs.wisc.edu/htcondor/>`_.
+- **Visualization**: Produce ternary plots of the flavour composition using the `python-ternary <https://zenodo.org/badge/latestdoi/19505/marcharper/python-ternary>`_ package and joint posterior plots for analyzing MCMC chains using the `getdist <https://getdist.readthedocs.io/en/latest/>`_ package.