zimmerst / dwarfjfactors

python dwarfs jvalue code

ASTROJPY

The Python script ASTROJPY allows to build the (-log) profile Likelihood for the astrophysical factor, J, of Dwarf Spheroidal Satellite Galaxies (dSPhs) of the Milky Way. It uses as input the kinematic data from the dSphs member stars. It also performs a basic statistical analysis, consisting in the determination of the Maximum Likelihood value of J and its Confidence Intervals. This README contains the instructions on how to use it correctly.

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FUNCTIONS.PYX MODULE This file contains the definitions of various functions used by the main script (ASTROJPY). For a faster execution, these are written in a format compatible with Python Cythonize package. In order to use it, it must first be compiled with a C++ compiler. A Python script which does this is also included, "setup.py", which should be executed with the following command $ python setup.py build_ext --inplace

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DATA INPUT Data should be input into the code as a three-columns datafile consisting of

1. the distance of a star from the dSphs center projected onto the sky (in units of kpc)
2. measured line-of-sight velocity of each star (in units of km/s)
3. measurement error on the line-of-sight velocity in (in units of km/s)

Alternatvely, the user can customize the data input by modifying the function get_data(gal) contained in functions.pyx

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DATA OUTPUT The code produces two files:

a) "Like.npy" consists of the 2D, vertically stacked array of the profile likelihood components of the dSphs J factor. Its extension means that it is a python numpy-saved objected, thus loadable with the command np.load inside a python script. J is given in log10 basis and has units of Gev^2/cm^5.

b) "results.yaml" contains a python dictionary with the results of the statistical analysis on the profile Likelihood curve. These consist of: its minimum (corresponfing to the Maximum Likelihood J value); the edges of the 1,2 and 3sigma confidence intervals of the minimum; the velocity anisotropy parameter (r_a in the Osipkov-Merrit case, b in the Charbonnier case)

In order to visualize the likelihood curve, we include a Python plotting script called "plot.py"