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@@ -4,11 +4,55 @@  [](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/).  +## 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 |