Codes & Parameters¶

N-body¶

Parameters Used¶

For the simulation suite we use P-Gadget3 (first 24 halos) and Gadget4 (remainder). The parent directory of each folder contains .gadget3 or .gadget4 to indicate which halo was run with which code. We use the following compile flags for our runs. PMGRID changes depending on the resolution of the run. There is a great amount of documentation on each of these parameters on Phil Hopkin’s GIZMO website.

PERIODIC
UNEQUALSOFTENINGS
PMGRID=512
GRIDBOOST=2
PLACEHIGHRESREGION=2
ENLARGEREGION=1.2
MULTIPLEDOMAINS=16
PEANOHILBERT
WALLCLOCK
MYSORT
DOUBLEPRECISION
OUTPUT_IN_DOUBLEPRECISION
INPUT_IN_DOUBLEPRECISION
ORDER_SNAPSHOTS_BY_ID
NO_ISEND_IRECV_IN_DOMAIN
FIX_PATHSCALE_MPI_STATUS_IGNORE_BUG
COMPUTE_POTENTIAL_ENERGY
OUTPUTPOTENTIAL
RECOMPUTE_POTENTIAL_ON_OUTPUT
HAVE_HDF5
DEBUG


Parameter files for each of the runs can be found as param.txt in the relevant simulation directory. For those users who have access to antares.mit.edu (MKI’s compute cluster), Brendan Griffen’s home directory contains the master files (configuration files, parameter files and expansion factor lists for output) for the entire suite.

/home/bgriffen/exec/gadget3


Halo Identification¶

Here you can find details about the halo identification methods we used.

Rockstar¶

Rockstar identifies dark matter halos, substructure, and tidal features in phase space.The approach is based on adaptive hierarchical refinement of friends-of-friends groups in six dimensions, which allows for robust (grid-independent, shape-independent, and noise-resilient) tracking of substructure; as such, it is named Rockstar (Robust Overdensity Calculation using K-Space Topologically Adaptive Refinement). The “consistent trees” algorithm for generating merger trees and halo catalogs explicitly ensures consistency of halo properties (mass, position, velocity, radius) across timesteps. The algorithm has demonstrated the ability to increase both the completeness (through inserting otherwise missing halos) and purity (through removing spurious objects) of both merger trees and halo catalogs. In addition, the method is able to robustly measure the self-consistency of halo finders; it is the first to directly measure the uncertainties in halo positions, halo velocities, and the halo mass function for a given halo finder based on actual cosmological simulations.

The code’s paper can be found here. Documentation on getting it running and a more exhaustive documentation of the catalogue’s content at its Bitbucket Repository.

Iterative Unbinding¶

Rockstar is able to find any overdensity in 6D phase space including both halos and streams. To distinguish gravitationally bound halos from other phase space structures, Rockstar performs a single-pass energy calculation to determine which particles are gravitationally bound to the halo. Over-densities where at least 50% of the mass is gravitationally bound are considered halos, with the exact fraction a tuneable parameter unbound_threshold of the algorithm Behroozi et al. (2013).

This definition is generally very effective at identifying halos and subhalos – but it fails in two important situations. First, if a subhalo is experiencing significant tidal stripping, the 50% cutoff can remove a subhalo from the catalog that should actually exist. We have found that changing the cutoff can recover the missing subhalos, but the best value of the cutoff is not easily determined. Second, Rockstar is occasionally too effective at finding substructure in our high resolution simulations. In particular, it often finds velocity substructures in the cores of our halos that are clearly spurious based on their mass accretion histories and density profiles. Importantly, these two issues do not just affect low mass subhalos, but they can also add or remove halos with $$V_{max}$$ > 25 km/s.

Both of these problems can be alleviated by applying an iterative unbinding procedure. We have implemented such an iterative unbinding procedure within Rockstar. At each iteration, we remove particles whose kinetic energy exceeds the potential energy from other particles in that iteration. The potential is computed with the Rockstar Barnes-Hut method (see Appendix B of Behroozi et al. (2013). We iterate the unbinding until we obtain a self-bound set of particles. Halos are only considered resolved if they contain at least 20 self-bound particles. All halo properties are then computed as usual, but with the self-bound particles instead of the one-pass bound particles. The iterative unbinding recovers the missing subhalos and removes most but not all of the spurious subhalos. Across 13 of our Caterpillar halos, we recover 52 halos with subhalo masses above 108 $$M_:raw-latex:odot$$ which would have otherwise been lost using the conventional Rockstar. See Griffen et al. (2015) for further details.

Full Particles Binary¶

Summary of changes for full particle binary output. 8/19/2014 (Alex Ji)

Alex added a Rockstar option to be able to output in binary all of the particle ids belonging to each halo. This means there is no need to recursively get particles from subhalos in RSDataReader. It also means particles from subhalo fof groups that were deemed unbound or too small, and hence not previously written out, are now correctly written out to the halo they should belong to.

Usage: Compiling When compiling Rockstar, use the command % make full_particle_binary This automatically compiles with HDF5 also.

Config File¶

In the Rockstar config file, specify FULL_PARTICLE_BINARY = num processors to write out full binary files This should be set to the same as NUM_WRITERS. It could be less, but I don’t see any reason for that. Also specify DELETE_BINARY_OUTPUT_AFTER_FINISHED = 1 This deletes the standard .bin files. The new files contain all of the same info as the old .bin files, so they are just a waste of space.

Inside haloutils.py we use RSDataReader.py version 10. There is now a new parameter called total_npart which specifies the total number of particles belonging to a halo, including substructure.

• get_particles_from_halo() will now return all particles from a halo.
• get_all_particles_from_halo() returns exactly the same thing except when using old version of rockstar.

he first npart particles from any given halo are exactly the same particles you would have gotten in the original Rockstar output.

• the files now end with the extension .fullbin instead of .bin

They are ~10% larger due to the extra particles in version 10.

Files Modified: config.template.h Makefile halo.h properties.c meta_io.c io_internal.c io_internal.h In config file, will need to specify FULL_PARTICLE_BINARY = num processors to write out full binary files This config option is now added to config.template.h and by default set to 0. When compiling, specify the option: $make full_particle_binary defines the flag FULL_PARTICLE_BINARY_FLAG This will compile a version such that the halo struct has an extra parameter total_num_p, found in halo.h. It will also compile with hdf5 since this is the case we most often want. total_num_p is assigned in properties.c in the function calc_additional_halo_props() This corresponds to the total number of particles belonging to the halo, including particles assigned to fof groups that were not printed out (for being unbound, too small, etc.) The extra parameter for each halo needs to be read in with RSDataReader.py version 7. it is named ‘total_npart’ for now. In the file meta_io.c, in the function output_halos() Added another if statement that calls output_full_particles_binary() to print data as we want it. Function called only if FULL_PARTICLE_BINARY > 0 as specified in config file. Wrote output_full_particles_binary() to io_internal.c. It is just like output_binary() except it uses a recursive function, print_child_particles_binary() to print all of the particles, not just num_p of them. Declared output_full_particles_binary() in io_internal.h print_child_particles_binary() in io_internal.c is my function which is a modification of print_child_particles() to recursively trace child halos and print their particles ids to binary, not ascii. print_child_particles_binary() did not need to be declared in any header file. Just written in file before function that calls it. Output files will now be named halos_x.y.fullbin x is the snap number y is the chunk number. Ranges from 0 to FULL_PARTICLES_BINARY-1 In the halo output, p_start corresponds to the starting position in the particle array p of the processor it is on where particles are belonging to that halo. We write this out as numstart in RSDataReader. It was not used before, and now shouldn’t ever be used. At the moment, cat.total_particles will not be truly accurate. It will refer to the sum of npart of every halo. It would be easy to change this to refer to the sum of total_npart for each halo in RSDataReader. I don’t see a good use for cat.total_particles either way though. The binary header information will specify num_particles as the sum of npart, or num_p. Changing this is a bad idea, as it will break other parts of how Rockstar is run. To specify a different particle count, such as number of unique particles written to file, or sum of total_num_p, would require an extra parameter to the struct binary_output_header, or to change num_particles just in the output. I don’t see a good use for this right now. Another note: Files are not written out in order to keep halo ids written out in order. Ex: halos.0.bin might write halos 0 – 250 halos.1.bin would write out halos 1001-1250 and halos.2.bin would write out halos 251-500. This means the unsorted data[i] array in RSDataReader will not always have the property that data[i] is the halo of id = i. Running Rockstar¶ So you have a Gadget simulation output and you want to run Rockstar on it (i.e. the same version as in Caterpillar). First you’ll need a working version of Rockstar which works with the read modules that we will be using later. The version we used for Rockstar can be found here. The output (when put int he appropriate folders, will work with out modules. Be sure to also get consistent-trees-0.9.9.2 to connect it all together. consistent-trees isn’t the latest version as were were a victum of the times; we had to go with one version, for all time. 1. Go to your Rockstar-hdf5 directory and run “make with_hdf5”. If it breaks, you need to point to the hdf5 libraries on your system. HDF5_FLAGS = -DH5_USE_16_API -lhdf5 -DENABLE_HDF5 -I/bigbang/data/bgriffen/lib/hdf5-1.8.10/include -L/bigbang/data/bgriffen/lib/hdf5-1.8.10/lib -lhdf5 -lz  1. Go to your consistent-trees directory and run “make all”. 2. Make a folder inside your Rockstar-hdf5/ directory called “cfgs” where you will put your cfg files. The parent simulation configuration file (parent.cfg) for the MIT halos looks like: PARALLEL_IO = 1 PARALLEL_IO_SERVER_INTERFACE = "ib0" FORK_READERS_FROM_WRITERS = 1 FORK_PROCESSORS_PER_MACHINE = 16 NUM_WRITERS = 128 INBASE = /bigbang/data/AnnaGroup/caterpillar/parent/gL100X10/outputs FILENAME = snapdir_<snap>/snap_<snap>.<block>.hdf5 NUM_BLOCKS = 256 FILE_FORMAT = "AREPO" FULL_PARTICLE_CHUNKS = 1 OUTBASE=/bigbang/data/AnnaGroup/caterpillar/parent/gL100X10/Rockstar AREPO_LENGTH_CONVERSION=1 AREPO_MASS_CONVERSION=1e+10 SNAPSHOT_NAMES=/bigbang/data/AnnaGroup/caterpillar/parent/gL100X10/Rockstar/snapparent.dat FORCE_RES=0.002 OUTPUT_FORMAT = "BOTH"  You’ll have to modify this to suite your needs. This is suited for infiniband using 16 cores per machine and NUM_WRITERS = Nnodes*Ncores being used. NUM_BLOCKS is also for the number of input files for a given snapshot which you specified for Gadget. You can leave it as AREPO as this is just default for using the HDF5 snapshots. You just need to make sure you set the LENGTH and MASS conversions correctly. The ones above are native for P-Gadget3. Be also sure to get FORCE_RES or force softening correct. Here we use 1/40 times the mean inter-particle separation or (1/40)*boxwidth/(np)^(1/3) which in our case is 100/2^10/40 ~ 0.002. If you have the space, I would suggest leaving OUTPUT_FORMAT to be BOTH. You can clean up the files you don’t want later. Snapshot names is just a file listing the snapshot names 000,001,002 ... up to the number of snaps, 127 in this case. 1. You will then have to submit the Rockstar run to the cluster. For a cluster like Stampede or other SLURM build systems you can adapt the following: #!/bin/bash #SBATCH -o parentrock.o%j #SBATCH -e parentrock.e%j #SBATCH -t 48:00:00 #SBATCH -p normal #SBATCH [email protected] #SBATCH -J parentrock #SBATCH --mail-type=ALL #SBATCH -n 512 module load hdf5 rsdir=/home1/02670/bgriffen/Rockstar exe=/home1/02670/bgriffen/Rockstar/rockstar cd$rsdir
outdir=/scratch/02670/bgriffen/gL100X10/Rockstar

$exe -c$rsdir/cfgs/parent.cfg &
#$exe -c$outdir/restart.cfg & #only use for restarting the run, comment line above.
cd $outdir perl -e 'sleep 1 while (!(-e "auto-rockstar.cfg"))' srun -n 512$exe -c auto-rockstar.cfg


If you are running on PBS, please email Brendan Griffen for a PBS version which works just as well.

SUBFIND¶

For a nice summary of how SUBFIND works, please see this talk by Volker Springel, the code author.

To access the SUBFIND catalogues and associated smoothing lengths for the particles, head to any Caterpillar halo directory on bigbang.mit.edu. There you will find the following:

H1387186_EB_Z127_P7_LN7_LX14_O4_NV4
-> outputs/      # gadget raw snapshot output (particle data)
-> groups_319/  # the subfind catalogues are also stored (mostly for the last snapshot)
-> hsmldir_319/ # the smoothing lengths for the corresponding particle data
-> analysis/     # post-processed output files (halo profiles, mass functions, minihalos etc.)


Merger Trees¶

consistent-trees¶

The code’s paper can be found here. Documentation on getting it running and a more exhaustive documentation of the catalogue’s content at its Bitbucket Repository.