master	version	DOI

This code has resulted in the publication arXiv:1703.09721. This code calculates the likelihood function for a model comprised of two components to the astrophysical neutrino flux detected by IceCube. The first component is extragalactic. Since point sources have not been found and there is increasing evidence that one source catalog cannot describe the entire data set, we model the extragalactic flux as isotropic. The second component is galactic. A variety of catalogs of interest exist here as well. We take the galactic contribution to be proportional to the matter density of the universe.

The likelihood function has one free parameter f_gal that is the fraction of the astrophysical flux that is galactic. The code finds the best fit value of f_gal and scans over 0<f_gal<1.

See the dependency guide and the installation guide.

The IceCube events are read in and managed in src/ICEvent.cpp. The likelihood function is described in src/Backgrounds.cpp and src/Likelihood.cpp. The von Mises-Fisher distribution is handled in vMF.cpp. A Markov Chain Monte Carlo code to generate points distributed in the galactic plane is in src/MWDisks.cpp. Finally, src/Figures.cpp generates data files that can then be plotted by the scripts in the py directory and src/main.cpp indicates what should be run.

1. Calculating the integral in equation 4.6 in the paper is the main time consuming portion of the code, other than generating the sky map and the galactic plane visualization. With this in mind, the code writes the calculation to file, data/L_gals.txt with calc_L_gals() and the result is read in to a global variable with read_in_gals().

2. The high energy cut on the galactic component mentioned in the paper can be turned on with the bool flag in calc_L_gals().

3. To turn off the progress bar on the slower functions, uncomment the Progress_Bar_visible = false; line in src/main.cpp.

4. To turn on a broken power law, change Phi_astro in src/Backgrounds.cpp and modify Phi_astro2 as necessary.

If you have questions or encounter any problems when running ANA, please use github's issue tracker.

This code is free to use, copy, distribute, and modify. If you use this code or any modification of this code, we request that you reference both this code DOI:10.5281/zenodo.438675 and the paper arXiv:1703.09721.