Overview & Basic Approach
Last updated
Last updated
The general approach of Caterpillar was that of the zoom-in technique adopted by many other groups, albeit with a few differences. we performed a much, much larger search for optimal computer parameters to ensure that contamination volumes are as large as possible without great cost. We also implemented iterative unbinding in ROCKSTAR which we found to be critical in identifying halos at the highest resolution simulations, particularly on highly radial orbits.
The volume of the parent simulation was selected to be 100 Mpc/h as this allows for roughly ~6500 Milky Way-sized (i.e. 10^12 Msol) systems to be found. After a gentle selection over local environment (i.e. making sure no halos were near clusters) 2122 candidates were used to select Caterpillar candidates.
We required a resolution which allowed us to resolve halos with 10,000 particles so as to construct well defined lagrangian volumes. This resulted in us selecting a resolution of 1024^3 or a particle mass of .
We selected halos with the following environmental requirements:
halos between 0.7 - 3 x 1012 \(M_:raw-latex:odot\) (6564 candidates)
no halos larger than 7 x 1013 \(M_:raw-latex:odot\) within 7 Mpc
no halos larger than 7 x 1012 \(M_:raw-latex:odot\) within 2.8 Mpc (2122 candidates)
This is roughly in line with Tollerud et al. (2012), Boylan-Kolchin et al. (2013), Fardal et al. (2013), Pfiffel et al. (2013), Li & White (2008), van der Marel et al. (2012), Karachentsev et al. (2004) and Tikhonov & Klypin (2009). This avoids Milky Way-sized systems near clusters but does not make them overly isolated necessarily. Halos were also selected to not be preferentially near the very edge of the simulation volume as a matter of convenience. The first 24 Caterpillar halos are highlighted within the parent volume below.
The time steps were set to be log of the expansion factor, following a similar convention to that used by the Millenium and Millenium-II simulations. The following table shows the various measures for time/size at each snapshot.
Nearly all of the following can be found in our flagship paper Griffen et al. (2015).
Lower Resolution Runs
