Investigation into the shape of the inner DM halo with stream kinematics.
This repository contains the code associated with the paper Bovy,
Bahmanyar, Fritz, & Kallivayalil (2016), which you should cite if you
re-use any of this code (in addition to, most likely,
galpy). The ipython notebooks used
to generate the plots in this paper can be found in the py/
directory of this repository. This code uses
galpy.
There are three useful notebooks, in addition to others that were used during code development that are not described further here.
(render this notebook on nbviewer, where you can toggle the code)
This notebook contains the fits of sections 2 and 6 of three-component Milky-Way potential models to a variety of dynamical data and the newly derived Pal 5 and GD-1 measurements. A variety of fits are explored, most of which are described in the paper. The full set of two-dimensional PDFs is also incldued for each fit. Figures 1 and 9 in the paper are produced by this notebook. Figure 10 of the best-fit force field and the constraints from disk stars, Pal 5, and GD-1 data is also made by this notebook.
(render this notebook on nbviewer, where you can toggle the code)
This notebook explores the constraints on the Milky Way's gravitational potential from the Pal 5 stream data. Most of the code to predict the stream track for Pal 5 is actually contained in pal5_util.py, which is used in this notebook to compute stream tracks (Figure 2). The MCMC exploration of the 32 potential families is performed by the mcmc_pal5.py code. The results of the MCMC (Figures 3, 4, and 5 in the paper) are analyzed in this notebook.
The MCMC analyses were run with commands like
python mcmc_pal5.py -i 0 -o ../pal5_mcmc/mwpot14-fitsigma-0.dat --dt=600. --td=10. --fitsigma -m 6
(render this notebook on nbviewer, where you can toggle the code)
This notebook explores the constraints on the Milky Way's gravitational potential from the GD-1 stream data. Most of the code to predict the stream track for GD-1 is actually contained in gd1_util.py, which is used in this notebook to compute stream tracks (Figure 6). This code is very similar to the Pal 5 code above. The MCMC exploration of the 32 potential families is performed by the mcmc_gd1.py code. This code is again very similar to the Pal 5 MCMC code above, but is slightly different because of the different parameterization of the progenitor and stream properties. The results of the MCMC (Figures 7 and 8 in the paper) are analyzed in this notebook.
The MCMC analyses were run with commands like
python mcmc_gd1.py -i 0 -o ../gd1_mcmc/mwpot14-0.dat --dt=1440. --td=10. -m 7