Our group uses N-body (gadget-2) ray-tracing (LensTools) simulations to study the weak gravitational lensing signature of large scale structure and to understand fundamental physics such as the nature of dark energy and the
total mass of neutrinos. In particular, we try to capture the rich information that is beyond the traditional two-point statistics, using non-Gaussian statistics, such as peak counts, Minkowski Functionals, and higher order moments.
|Jose M. Zorrilla
|J. Colin Hill
For the full detail for our simulation pipeline see Petri 2016.
A briefer description can be found in the "simulation" section in either Zorrilla et al 2016, Liu et al 2016 , or Petri et al 2016 .
Each tar.gz file (15GB) has 1000 fits maps (17MB each).
Simulation configuration (also in the header of the fits files):
size of the box = 240 Mpc/h
source redshift z_s = 2.0
map size = 3.5 x 3.5 = 12.25 deg^2
number of particles = 512^3
resolution = 2048 x 2048 pixels
No shape noise
Fixed cosmological parameters:
h = 0.72
n_s = 0.96
Omega_b = 0.046
Tcmb = 2.725K
N_eff = 3.04
neutrino masses = 0, 0, 0
Varying parameters are in the file name (also in the fits header), for example:
Omega_m = 0.26
Omega_lambda = 0.74
w_0 = -0.8, w_a = 0, where the dark energy EoS is w(a)=w_0+(1-a)w_a.
sigma_8 = 0.80
B-mode maps (for null tests):
This is a set of simulated convergence maps for 96 different cosmologies. Each cosmology differs only on two cosmological parameters, the density of matter, Omega_m, and the amplitude of density fluctuations measured in the late universe, sigma_8. Each is saved in a compressed directory. Within the directory, there are 512 convergence maps, saved as fits files.
This dataset was used for the analyses presented in Matilla et al 2017 and Gupta et al 2018, where you can find detailed descriptions of the data. These two papers should be cited in a publication that makes use of the maps.
For convenience, here is a brief description:
- Each convergence map covers a field of view of 3.5deg x 3.5deg, and has a resolution of 1024 x 1024 pixels.
- Maps share the initial random seeds between cosmologies. That is, the map 0001 for cosmology a and the same map for cosmology b were generated using the same random seed, and will exhibit similar structures in the same regions.
- Maps represent noiseless convergence from sources at a constant redshift z=1.0.
- Each map was generated ray-tracing the outputs of dark matter-only, N-body simulations, using the multi plane algorithm implemented in Lenstools. The ray-tracing does not use the Born approximation, but assumes a flat sky.
- Each past light-cone was built from snapshots from a single N-body simulation for each cosmology. The simulations evolved a 240Mpc/h side cube with 512^3 dark matter particles, using GADGET2. The distance between planes corresponds to 80Mpc/h on the fiducial cosmology (Omega_m=0.260, sigma_8=0.800).
- Initial conditions for the Nbody simulations were built using NGenIC, from scaled power spectra computed with CAMB.
Sample data: Download
Full data (set of maps in all 96 cosmologies) can be downloaded from here, please email email@example.com for a guest username/password.
Note that a few maps were lost during a file transfer to a permanent repository and these two cosmological models have fewer maps:
Om0.246_si0.926: 508 maps
Om0.251_si0.807: 455 maps
When our maps are used in your paper, please:
(1) Acknowledge the grant that supported our creation of these maps: NSF grant AST-1210877 and NSF XSEDE allocation AST-140041.
(2) Cite "Mocking the Weak Lensing universe: the LensTools python computing package" Petri 2016. The python code LensTools, which we used to create our maps, can be found at: lenstools.readthedocs.io
(3) For the MassiveNuS dataset, please cite Liu et al. 2018, and acknowledge the data hosting facility: "We thank New Mexico State University (USA) and Instituto de Astrofisica de Andalucia CSIC (Spain) for hosting the Skies & Universes site for cosmological simulation products."
(4) For the Dark Matter dataset, please cite Matilla et al 2017 and Arushi et al 2018.