Table of contents


Back to the main page 1. Download 2. How to load it in Python 3. Cosmology 4. Tracer sample 5. Void samples 6. Reconstruction 7. Dictionary keys
For tests of the void-galaxy CCF theory model using this dataset see this page.


Void data


Here we have a compilation of a lot of data relevant for working with the void-galaxy cross-correlation function. We have voids, from several different void finder algorithms and center definitions, found in both realspace, redshiftspace and in reconstruction space (pseudo realspace).

The data we provide is:

  • Radial cross-correlation functions (most combinations of halos either being in real/recon/rsd space crossed with voids being in real/recon/rsd space)
  • Cross-correlation function multipoles (most combinations of halos being in real/recon/rsd space and voids being in real/recon/rsd space)
  • DM profiles around voids in real/recon/rsd space
  • Halo profiles around voids in real/recon/rsd space
  • Power-spectrum of halos in real/recon/rsd space
  • Power-spectrum multipoles of halos in real/recon/rsd space

Download

Each datafile we provide is a simple JSON file that can be loaded as an array of dictionaries in python. There is one file per voidtype and a given reconstruction smoothing scale (fiducial 7.5 Mpc/h) and a void size cut (fiducial 30.0 Mpc/h). Each dictionary corresponds to 1 simulation and there are 100 simulations in total. If you aren't interested in reconstruction then just fetch any file and just don't use the RECON-data (the REAL and RSD only data in the files are the same in all the different files with different reconstruction scale).

Voxel (circumcenter) Voidfinder; $R_{\rm recon} = 5.0$ Mpc$/h$; $R_{\rm void~cut} = 30.0$ Mpc$/h$
Voxel (circumcenter) Voidfinder; $R_{\rm recon} = 7.5$ Mpc$/h$; $R_{\rm void~cut} = 30.0$ Mpc$/h$
Voxel (circumcenter) Voidfinder; $R_{\rm recon} = 10.0$ Mpc$/h$; $R_{\rm void~cut} = 30.0$ Mpc$/h$

Voxel (barycenter) Voidfinder; $R_{\rm recon} = 5.0$ Mpc$/h$; $R_{\rm void~cut} = 30.0$ Mpc$/h$
Voxel (barycenter) Voidfinder; $R_{\rm recon} = 7.5$ Mpc$/h$; $R_{\rm void~cut} = 30.0$ Mpc$/h$
Voxel (barycenter) Voidfinder; $R_{\rm recon} = 10.0$ Mpc$/h$; $R_{\rm void~cut} = 30.0$ Mpc$/h$

Zobov (circumcenter) Voidfinder; $R_{\rm recon} = 5.0$ Mpc$/h$; $R_{\rm void~cut} = 30.0$ Mpc$/h$
Zobov (circumcenter) Voidfinder; $R_{\rm recon} = 7.5$ Mpc$/h$; $R_{\rm void~cut} = 30.0$ Mpc$/h$
Zobov (circumcenter) Voidfinder; $R_{\rm recon} = 10.0$ Mpc$/h$; $R_{\rm void~cut} = 30.0$ Mpc$/h$

Zobov (barycenter) Voidfinder; $R_{\rm recon} = 5.0$ Mpc$/h$; $R_{\rm void~cut} = 30.0$ Mpc$/h$
Zobov (barycenter) Voidfinder; $R_{\rm recon} = 7.5$ Mpc$/h$; $R_{\rm void~cut} = 30.0$ Mpc$/h$
Zobov (barycenter) Voidfinder; $R_{\rm recon} = 10.0$ Mpc$/h$; $R_{\rm void~cut} = 30.0$ Mpc$/h$

Spherical Voidfinder; $R_{\rm recon} = 5.0$ Mpc$/h$; $R_{\rm void~cut} = 30.0$ Mpc$/h$
Spherical Voidfinder; $R_{\rm recon} = 7.5$ Mpc$/h$; $R_{\rm void~cut} = 30.0$ Mpc$/h$
Spherical Voidfinder; $R_{\rm recon} = 10.0$ Mpc$/h$; $R_{\rm void~cut} = 30.0$ Mpc$/h$

VIDE Voidfinder; $R_{\rm recon} = 7.5$ Mpc$/h$; $R_{\rm void~cut} = 30.0$ Mpc$/h$
VIDE Voidfinder; $R_{\rm recon} = 7.5$ Mpc$/h$; $R_{\rm void~cut} = 30.0$ Mpc$/h$
VIDE Voidfinder; $R_{\rm recon} = 7.5$ Mpc$/h$; $R_{\rm void~cut} = 30.0$ Mpc$/h$

How to load it in Python

For an bigger example read file see read.py.
import matplotlib.pyplot as plt
import numpy as np
import json

# Load the data
with open('data_voxel_reconR7.5_cut30.0.txt', 'rb') as json_file:
  data = json.load(json_file)

# Number of simulations
print("There are " + str(len(data)) + " simulations present")

# Simulation to plot below
isim = 0

# Fetch the DM density profile around the Halo voids in the first simulation
# The data is a list so if you want to use it as a numpy array 
# wrap the right hand side inside np.array( ... )
r     = data[isim]["profile_DM_REAL_Void_REAL_radius"]
delta = data[isim]["profile_DM_REAL_Void_REAL_delta"]

# Plot it
plt.plot(r,delta)
plt.show()
    

See below for a full list of dictionary keys.

Cosmology

The data is the fiducial cosmology from the Quijote simulations. Number of realisations $= 100$ (realisation 100,101,...,200 from the Quijote fiducial suite)
Simulation boxsize $= 1000$ Mpc$/h$
Simulation redshift $= 0.5$ (snap-num 003)
Simulation NpartDM $= 512^3$
$\Omega_M = 0.3175$
$\Omega_B = 0.049$
$h = 0.6711$
$n_s = 0.9624$
$\sigma_8 = 0.834$

Tracer sample

Halo sample consists of all halos provided in the Quijote suite. This is halos with $\geq 20$ particles and has a mass $\geq 10^{13.12} M_{\rm sun}/h$. The bias of the sample is $b \simeq 1.95$.

Void samples

We have 6 void types: Spherical, Zobov (circumcenter), Voxel (circumcenter), Voxel (barycenter), Zobov (barycenter) and VIDE. The fiducial void size cut is R = 30 Mpc/h. Here VIDE is the original VIDE algorithm which is a Zobov with barycenter catalog with voids that have too high ($\geq 0.2$) central density removed. Void_REAL means voids located using the realspace positions of the halos, Void_RECON means voids located using the reconstred positions of the halos and Void_RSD means voids located using the redshiftspace positions of the halos.

Reconstruction

Reconstruction is used to get pseudo-realspace positions for the halos and then running void finding on this sample. This is used since the theory model we use require realspace voids (and we don't have access to realspace positions of our tracers in observations). We have run reconstruction with several choices for the smoothing scale. The "best" smoothing scale (the one that minimizes the quadrupole of the post-reconstruction halo-halo power-spectrum) is $R = 7.5$ Mpc$/h$.

Dictionary keys

There are ~100 keys availiable. The names are hopefully understandable, e.g. CCF = cross-correlation function, pofk = auto power-spectrum, Halo_Y means halos in Y-space, Void_Y means voids computed from the halos in Y-space, multipoles are Legendre multipoles, sigma is the line-of-sight velocity dispersion and v is the radial velocity. A list of most dictionary keys is given below. For a complete list load the data and use .keys() to list them all.


# Radial cross-correlation functions
CCF_radial_Halo_REAL_Void_REAL_radius
CCF_radial_Halo_REAL_Void_REAL_xir

CCF_radial_Halo_REAL_Void_RECON_radius
CCF_radial_Halo_REAL_Void_RECON_xir

CCF_radial_Halo_REAL_Void_RSD_radius
CCF_radial_Halo_REAL_Void_RSD_xir

CCF_radial_Halo_RSD_Void_RSD_radius
CCF_radial_Halo_RSD_Void_RSD_xir

CCF_radial_Halo_RSD_Void_RECON_radius
CCF_radial_Halo_RSD_Void_RECON_xir

CCF_radial_Halo_RECON_Void_RECON_radius
CCF_radial_Halo_RECON_Void_RECON_xir

# Multipoles of cross-correlation functions
CCF_multipole_Halo_RSD_Void_RSD_radius
CCF_multipole_Halo_RSD_Void_RSD_xi0
CCF_multipole_Halo_RSD_Void_RSD_xi2
CCF_multipole_Halo_RSD_Void_RSD_xi4

CCF_multipole_Halo_RSD_Void_REAL_radius
CCF_multipole_Halo_RSD_Void_REAL_xi0
CCF_multipole_Halo_RSD_Void_REAL_xi2
CCF_multipole_Halo_RSD_Void_REAL_xi4

CCF_multipole_Halo_RSD_Void_RECON_radius
CCF_multipole_Halo_RSD_Void_RECON_xi0
CCF_multipole_Halo_RSD_Void_RECON_xi2
CCF_multipole_Halo_RSD_Void_RECON_xi4

CCF_multipole_Halo_RECON_Void_RECON_radius
CCF_multipole_Halo_RECON_Void_RECON_xi0
CCF_multipole_Halo_RECON_Void_RECON_xi2
CCF_multipole_Halo_RECON_Void_RECON_xi4

# Profiles
profile_DM_REAL_Void_REAL_radius
profile_DM_REAL_Void_REAL_delta
profile_DM_REAL_Void_REAL_Delta
profile_DM_REAL_Void_REAL_v
profile_DM_REAL_Void_REAL_sigma

profile_DM_REAL_Void_RECON_radius
profile_DM_REAL_Void_RECON_delta
profile_DM_REAL_Void_RECON_Delta
profile_DM_REAL_Void_RECON_v
profile_DM_REAL_Void_RECON_sigma

profile_Halo_REAL_Void_REAL_radius
profile_Halo_REAL_Void_REAL_delta
profile_Halo_REAL_Void_REAL_Delta
profile_Halo_REAL_Void_REAL_v
profile_Halo_REAL_Void_REAL_sigma

profile_Halo_REAL_Void_RECON_radius
profile_Halo_REAL_Void_RECON_delta
profile_Halo_REAL_Void_RECON_Delta
profile_Halo_REAL_Void_RECON_v
profile_Halo_REAL_Void_RECON_sigma

profile_Halo_REAL_Void_RSD_radius
profile_Halo_REAL_Void_RSD_delta
profile_Halo_REAL_Void_RSD_Delta
profile_Halo_REAL_Void_RSD_v
profile_Halo_REAL_Void_RSD_sigma

profile_Halo_RSD_Void_RSD_radius
profile_Halo_RSD_Void_RSD_delta
profile_Halo_RSD_Void_RSD_Delta
profile_Halo_RSD_Void_RSD_v
profile_Halo_RSD_Void_RSD_sigma

# Power-spectrum
pofk_Halo_REAL_k
pofk_Halo_REAL_Pk

pofk_DM_REAL_k
pofk_DM_REAL_Pk

# Power-spectrum multipoles
pofk_multipole_Halo_REAL_k
pofk_multipole_Halo_REAL_Pk0
pofk_multipole_Halo_REAL_Pk2
pofk_multipole_Halo_REAL_Pk4

pofk_multipole_Halo_RECON_k
pofk_multipole_Halo_RECON_Pk0
pofk_multipole_Halo_RECON_Pk2
pofk_multipole_Halo_RECON_Pk4

pofk_multipole_Halo_RSD_k
pofk_multipole_Halo_RSD_Pk0
pofk_multipole_Halo_RSD_Pk2
pofk_multipole_Halo_RSD_Pk4