Milky Way-est Data#
Suite Information#
For information about the Milky Way hosts, LMC analogs, and GSE analogs in the Milky Way-est suite, please see Table 2 of Buch et al. (2024).
We use the following cosmological parameters in our simulations: \(h=0.7, \Omega_{\rm m} = 0.286, \Omega_{\Lambda} = 0.714, \sigma_8 = 0.82, n_s=0.96\).
For the definitions of these parameters, see the symlib.simulation_parameters description in symlib Documentation.
Initial conditions for the zoom-in simulations were generated using MUSIC (Hahn and Abel (2011)), the simulations themselves were run using GADGET-2 code (Springel (2005)), and we generated halo catalogs and merger trees using ROCKSTAR (Behroozi et al. (2013a)) and CONSISTENT-TREES (Behroozi et al. (2013b)).
Data Access#
There are 2 approaches to using Milky Way-est data: 1) load symlib-compatible files stored at s3df.slac.stanford.edu/data/kipac/symphony, or 2) load simulation analysis output like the Milky Way-est authors with files stored at s3df.slac.stanford.edu/data/kipac/symphony/mwest.
You can load whichever data format suits your needs!
Both have the same access point:
Analysis Options#
How would you like to make use of the Milky Way-est suite?
Use symlib functionality with Milky Way-est
This is a great option for folks who already use symlib tools for their existing data analysis pipeline, want to make use of the functions and documentation that symlib offers, or otherwise want a more guided/abstracted approach to working with these halos. See the The Symlib Analaysis Library page for more information.
Load Milky Way-est halo catalogs directly
This might be a good option for you if, upon gaining access to the data files, you’d like to get started making plots and iterating directly from the halo catalogs themselves. This is how the authors approached their analysis, and we’ve put together a pipeline you can replicate to directly load and analyze the data. See Milky Way-est Analysis Notebooks for more!