add features of GolemFlavor to README

1 files changed, 49 insertions, 5 deletions

diff --git a/README.md b/README.md
index b25cb55..786a8cf 100644
--- a/README.md
+++ b/README.md
@@ -4,11 +4,55 @@
 ![Python Version](https://img.shields.io/badge/python-2.7+|3.4+-blue.svg)
 [![license](https://img.shields.io/github/license/ShiveshM/GolemFlavor 'license')](https://github.com/ShiveshM/GolemFlavor/blob/master/LICENSE)
 
-GolemFlavor is a Python package for running an analysis pipeline using
-`GolemFit`.
+GolemFlavor is a Python package for running a Bayesian inference analysis
+pipeline using Astrophysical Flavor data taken at
+[IceCube](https://icecube.wisc.edu/).
 
 ![GolemFlavor Logo](logo.png)
 
+## Overview
+
+### What is Astrophysical Flavor data?
+This is data of the *flavor* of a
+[neutrino](https://icecube.wisc.edu/outreach/neutrinos) 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 [black
+holes](https://doi.org/10.1126/science.aat2890). For more on the physics behind
+neutrinos see TODO.
+
+### 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 TODO.
+
+## 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.
+
+## Getting Started
+
+
 ## Installation
 GolemFlavor can be installed using `pip`
 ```
@@ -19,9 +63,9 @@ NumPy and SciPy.
 
 GolemFlavor uses the IceCube software [`GolemFit: The HESE
 fitter`](https://github.com/IceCubeOpenSource/GolemFit) to fit with IceCube
-HESE data. Current access is limited to IceCube collaborators. A simple
-Gaussian likelihood can be used instead for test purposes if this requirement
-is not found.
+Astrophysical Flavor data. This software is proprietary and so access is
+currently limited to IceCube collaborators. A simple Gaussian likelihood can be
+used as a substitute for test purposes if this requirement is not found.
 
 ### Dependencies