DWARFedMASSES is a Markov Chain Monte Carlo based algorithm to constrain the masses of tidally disrupting satellite galaxies. We constrain the mass of the Carina dwarf spheroidal galaxy both today and before infall into the Milky Way 6 Gyr ago. The details of the project can be found in this article.

This page includes all the data from the Nbody simulations for 6 sets of MCMC chains run for this project. The sets differ from each other in the dark matter halo profile that was used for the models and the observational data they were compared to.

You can access the chains from the following links:

Each Chain directory listed above has a parameter file with the values that combine all the model parameters used in our paper as well as the input and output binary files from the Nbody simulations.

The observational data for Carina can be accessed here: Carina surface brightness profile, and Carina velocity dispersion profile.

The model for the Mock dwarf is here: Mock dwarf initial state, and Mock dwarf final state. The noisy profiles used in the changes are in Mock dwarf surface brightness and Mock dwarf velocity dispersion.

The MCMC pipeline was built using codes from several people in addition to our own. Any codes we share here are made public with the purpose of transparency and reproducibility. However, they come with no warranty of being bug free and there is noone currently working on them who can provide support.

The general packages and tools you need are the GalPot and falcON codes (written by Walter Dehnen) which can be obtained within the NEMO toolbox, as well as SWIG, PKDGRAV, Python.

The Nbody simulations were made using pkdGrav (written by Joachim Stadel). We used version pkdgrav1.1 in which we implemented the external potential provided by the GalPot. The current version of pkdGrav can be found here.

The pytipsy library (written by Doug Potter) needed to read the binary files of the simulation data and the orbit integration code (written by Hanni Lux) can be downloaded together from here: orbit . There is a short README file that might be useful.

The analysis codes calculating the profiles compared to the observations can be downloaded here: Analysis codes together with the rest of the codes used to run the MCMC. The README file might eb helpful They use the pytipsy library above to read the tipsy file in.

For any further comments or questions, please contact: