Overview

What is Astrophysical Flavor data?

This is data of the flavor of a neutrino taken at the IceCube neutrino observatory, 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. For more on the physics behind neutrinos see the 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*. For more information on the statistical modeling see the 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 and MultiNest.
• Anarchic Sampling: Sampling of the neutrino mixing matrix is done under the *neutrino mixing anarchy* hypothesis to ensure an unbiased prior.
• Distributed and parallel computing: Scripts included to manage the workload across a CPU cluster using HTCondor.
• Visualization: Produce ternary plots of the flavor composition using the python-ternary package and joint posterior plots for analyzing MCMC chains using the getdist package.