LSS Mocks - User contributions [en-ca]

Large Scale Structure Mocks

2020-02-25T19:15:18Z

Gstein:

Welcome to the CITA LSS Mocks Portal.

= [http://mocks.cita.utoronto.ca/websky WebSky Extragalactic CMB Mocks] =
Data to accompany [https://arxiv.org/abs/2001.08787 ''The Websky Extragalactic CMB Simulations'' - G. Stein, M. A. Alvarez, J. R. Bond, A. v. Engelen, N. Battaglia (2020)].

= [http://cita.utoronto.ca/~malvarez/research/ksz-data Patchy kSZ Maps] =
Data and figures to accompany [http://adsabs.harvard.edu/abs/2016ApJ...824..118A Alvarez (2016)].

File:KappaPower.png

2020-02-25T19:02:11Z

Gstein: Gstein uploaded a new version of File:KappaPower.png

File:Ksz.png

2020-02-25T18:59:44Z

Gstein: Gstein uploaded a new version of File:Ksz.png

File:KappaPower.png

2019-10-17T16:56:43Z

Gstein:

File:Cib.png

2019-10-17T16:53:23Z

Gstein: Gstein uploaded a new version of File:Cib.png

Large Scale Structure Mocks

2019-10-16T20:05:34Z

Gstein:

Welcome to the CITA LSS Mocks Portal.

= [http://mocks.cita.utoronto.ca/websky WebSky Extragalactic CMB Mocks] =
CIB intensity, Compton-y, CMB lensing convergence, lensed and unlensed CMB temperature, all from the same 12288^3, 15.4 Gpc simulation.

= [http://cita.utoronto.ca/~malvarez/research/ksz-data Patchy kSZ Maps] =
Data and figures to accompany [http://adsabs.harvard.edu/abs/2016ApJ...824..118A Alvarez (2016)].

File:Conversion factors.png

2019-08-15T19:44:21Z

Gstein: conversion factors to Tcmb units

conversion factors to Tcmb units

Final Work - Remi-links

2018-08-24T18:40:49Z

Gstein: Created page with "Lenspix, the code to lens healpix maps given an input kappa map, can be found here: https://github.com/louis-pham/modified_lenspix cal-sky, the code to create "field" maps..."

Lenspix, the code to lens healpix maps given an input kappa map, can be found here:

https://github.com/louis-pham/modified_lenspix

cal-sky, the code to create "field" maps of kappa, cib, etc, can be found here, where scripts to break up maps into redshift slices can be found in George Stein's fork:

https://github.com/marcelo-alvarez/cal-sky

Logbook

2018-08-24T18:38:52Z

Gstein:

Here is the research log for Remi's CIB lensing project. Links to research notes are below.

[[Final Work - Remi-links]]

[[August 17 - Cal-Sky]]

[[August 9 - Cal-Sky - CIB 2]]

[[July 20 - Cal-Sky - CIB]]

[[June 11 - CIB - Lensing]]

[[June 06 - CMB, Websky and LensPix]]

[[June 05 - LensPix parameters]]

[[May 14 - Issue with lensing]]

[[Apr 26 - First look at lensing sims]]

Logbook

2018-08-24T18:37:49Z

Gstein:

Here is the research log for Remi's CIB lensing project. Links to research notes are below.

[[August 17 - Cal-Sky]]

[[August 9 - Cal-Sky - CIB 2]]

[[July 20 - Cal-Sky - CIB]]

[[June 11 - CIB - Lensing]]

[[June 06 - CMB, Websky and LensPix]]

[[June 05 - LensPix parameters]]

[[May 14 - Issue with lensing]]

[[Apr 26 - First look at lensing sims]]

Documentation

2018-08-24T18:37:15Z

Gstein:

== Logbooks ==

Here are the logbooks from various WebSky related projects:

[[ Logbook | Remi CIB Lensing ]]

== Software ==

== Data ==

[http://cita.utoronto.ca/~malvarez/research/ksz-data/ Patchy kSZ Maps]

[https://www.dropbox.com/sh/deqz1gekpw0fapw/AABGKqqzG9wMnLP0LqFzE6i-a?dl=0/ Kappa, unlensed and lensed maps (LP)]

[https://www.dropbox.com/s/1evuw17snfqzhwj/all.pdf?dl=0 K, Phi, unlensed, lensed PDF (LP)]

== Transferring Data to Nersc ==

1.) log in to nersc - $ssh <username>@edison.nersc.gov

2.) log into edisongrid - $ssh edisongrid

3.) Sign into collaboration account
$module load globus
$myproxy-logon -s nerscca.nersc.gov !Password is the same as your username
$gsissh localhost -l cmbs4

4.) $cd /project/projectdirs/cmbs4

5.) ensure that the permissions are set appropriately (g+rX,o-rwx)

6.) Update README and cmbs4 wiki for any new files you add or changes you make!

README snippet for .npz catalogues
HALO CATALOGUES:
Contains (x,y,z,mass,redshift) for each halo. They are saved in the npz format of numpy (see http://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html). To load simply use
halo_cat = np.load(outfile)
xpk = halo_cat["xpk"]
ypk = halo_cat["ypk"]
zpk = halo_cat["zpk"]
M = halo_cat["M"]
redshift = halo_cat["redshift"]

README snippet for .pksc catalogues

Contains (x,y,z,v_x,v_y,v_z,R_TH) for each halo. The final halo catalog is a binary file with a 12 byte header, R_TH is the Lagrangian top hat radius of the halo. The conversion to mass is M_halo = 4*pi/3 * R_TH**3 * rho_bar, where rho_mean = 2.775e11*Omega_M*h**2.
header = (int Non, float RTHmax, float redshiftbox)
where Non is the total number of halos found,
RTHmax is the radius of the largest halo in the simulation,
redshiftbox is the redshift of the box, which will be negative for lightcone runs.
This 12 byte header is then followed by a list of Non*7 4 byte floats that represent (x_halo,y_halo,z_halo,vx_halo,vy_halo,vz_halo,Rth_halo)

Sample Python and Fortran code to load a datafile:

Python:
pkfile = open(filein,"rb")
Non = np.fromfile(pkfile, dtype=np.int32, count=1)
RTHMAXin = np.fromfile(pkfile, dtype=np.float32, count=1)
redshiftbox = np.fromfile(pkfile, dtype=np.float32, count=1)
print "Non = ", Non
npkdata = 7*Non
peakdata = np.fromfile(pkfile, dtype=np.float32, count=npkdata)
peakdata = np.reshape(peakdata,(Non,7))
xpos = peakdata[:,0]
ypos = peakdata[:,1]
zpos = peakdata[:,2]
vxpos = peakdata[:,3]
vypos = peakdata[:,4]
vzpos = peakdata[:,5]
Rth = peakdata[:,6]
Omega_M = 0.25
h = 0.7
rhomean = 2.775e11*Omega_M*h**2
M = 4.0/3*np.pi*Rth**3*rhomean

Redshifts
z=np.linspace(0,4,1000)
def hubble(z):
return h*100*np.sqrt(omegam*(1+z)**3+1-omegam)
def drdz(z):
return 3e5 / hubble(z)
r = np.cumsum(drdz(z)*(z[1]-z[0]))
r -= r[0]
z_to_r = sp.interpolate.interp1d(z,r)
r_to_z = sp.interpolate.interp1d(r,z)

Fortran:
open(4,file=filein,status='old',access='stream')
read(4) Npk,RTHLmax_in,boxredshift
allocate(posxyz(3,nhalo))
allocate(velxyz(3,nhalo))
allocate(rth(nhalo))
allocate(mass(nhalo))
allocate(vrad(nhalo))
offset_num_floats = 7*nhalo*myid
read(4) ((posxyz(j,i),j=1,3),&
(velxyz(j,i),j=1,3),&
rth(i),i=1,nhalo)
close(4)

== Modeling ==

=== CMB Lensing ===

Using a 'field+halo' model to generate lensing kappa maps from Gaussian random field (GRF) of density fluctuations on the past light cone.

'''Halo Model:''' The peak patch algorithm is run on the GRF to produce a catalog of halos with [RA, Dec, redshift, velocity, mass]. The halos are assumed to have a spherically-symmetric matter distribution parameterized by a generalized NFW density profile. This contribution to the convergence from each halo is then projected onto the kappa map, appropriately weighted by the lensing kernel, W(z).
<gallery mode=packed heights="300px">
File:fullskykappa.png|"Halo only" full sky kappa map
File:flatskykappa.png|"Halo only" zoomed in kappa map
</gallery>

'''Field Model:''' (development in progress) Lagrangian perturbation theory is used to move matter not within halos to its final Eulerian position. This field matter distribution is then added directly to the kappa map. A [260 x 260 x 60] Mpc/h slab at z=0 is shown below with all the matter moved with either first (1LPT) or second (2LPT) order Lagrangian perturbation theory, in addition to the halos found with the peak patch method (these peak patch halos are also moved with either . Shown below are the same peak patch halo distributions, but also the

Documentation

2018-08-24T18:37:03Z

Gstein: /* Logbooks */

== Logbooks ==

Here are the logbooks from various WebSky related projects:
[[ Logbook | Remi CIB Lensing ]]

== Software ==

== Data ==

[http://cita.utoronto.ca/~malvarez/research/ksz-data/ Patchy kSZ Maps]

[https://www.dropbox.com/sh/deqz1gekpw0fapw/AABGKqqzG9wMnLP0LqFzE6i-a?dl=0/ Kappa, unlensed and lensed maps (LP)]

[https://www.dropbox.com/s/1evuw17snfqzhwj/all.pdf?dl=0 K, Phi, unlensed, lensed PDF (LP)]

== Transferring Data to Nersc ==

1.) log in to nersc - $ssh <username>@edison.nersc.gov

2.) log into edisongrid - $ssh edisongrid

3.) Sign into collaboration account
$module load globus
$myproxy-logon -s nerscca.nersc.gov !Password is the same as your username
$gsissh localhost -l cmbs4

4.) $cd /project/projectdirs/cmbs4

5.) ensure that the permissions are set appropriately (g+rX,o-rwx)

6.) Update README and cmbs4 wiki for any new files you add or changes you make!

README snippet for .npz catalogues
HALO CATALOGUES:
Contains (x,y,z,mass,redshift) for each halo. They are saved in the npz format of numpy (see http://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html). To load simply use
halo_cat = np.load(outfile)
xpk = halo_cat["xpk"]
ypk = halo_cat["ypk"]
zpk = halo_cat["zpk"]
M = halo_cat["M"]
redshift = halo_cat["redshift"]

README snippet for .pksc catalogues

Contains (x,y,z,v_x,v_y,v_z,R_TH) for each halo. The final halo catalog is a binary file with a 12 byte header, R_TH is the Lagrangian top hat radius of the halo. The conversion to mass is M_halo = 4*pi/3 * R_TH**3 * rho_bar, where rho_mean = 2.775e11*Omega_M*h**2.
header = (int Non, float RTHmax, float redshiftbox)
where Non is the total number of halos found,
RTHmax is the radius of the largest halo in the simulation,
redshiftbox is the redshift of the box, which will be negative for lightcone runs.
This 12 byte header is then followed by a list of Non*7 4 byte floats that represent (x_halo,y_halo,z_halo,vx_halo,vy_halo,vz_halo,Rth_halo)

Sample Python and Fortran code to load a datafile:

Python:
pkfile = open(filein,"rb")
Non = np.fromfile(pkfile, dtype=np.int32, count=1)
RTHMAXin = np.fromfile(pkfile, dtype=np.float32, count=1)
redshiftbox = np.fromfile(pkfile, dtype=np.float32, count=1)
print "Non = ", Non
npkdata = 7*Non
peakdata = np.fromfile(pkfile, dtype=np.float32, count=npkdata)
peakdata = np.reshape(peakdata,(Non,7))
xpos = peakdata[:,0]
ypos = peakdata[:,1]
zpos = peakdata[:,2]
vxpos = peakdata[:,3]
vypos = peakdata[:,4]
vzpos = peakdata[:,5]
Rth = peakdata[:,6]
Omega_M = 0.25
h = 0.7
rhomean = 2.775e11*Omega_M*h**2
M = 4.0/3*np.pi*Rth**3*rhomean

Redshifts
z=np.linspace(0,4,1000)
def hubble(z):
return h*100*np.sqrt(omegam*(1+z)**3+1-omegam)
def drdz(z):
return 3e5 / hubble(z)
r = np.cumsum(drdz(z)*(z[1]-z[0]))
r -= r[0]
z_to_r = sp.interpolate.interp1d(z,r)
r_to_z = sp.interpolate.interp1d(r,z)

Fortran:
open(4,file=filein,status='old',access='stream')
read(4) Npk,RTHLmax_in,boxredshift
allocate(posxyz(3,nhalo))
allocate(velxyz(3,nhalo))
allocate(rth(nhalo))
allocate(mass(nhalo))
allocate(vrad(nhalo))
offset_num_floats = 7*nhalo*myid
read(4) ((posxyz(j,i),j=1,3),&
(velxyz(j,i),j=1,3),&
rth(i),i=1,nhalo)
close(4)

== Modeling ==

=== CMB Lensing ===

Using a 'field+halo' model to generate lensing kappa maps from Gaussian random field (GRF) of density fluctuations on the past light cone.

'''Halo Model:''' The peak patch algorithm is run on the GRF to produce a catalog of halos with [RA, Dec, redshift, velocity, mass]. The halos are assumed to have a spherically-symmetric matter distribution parameterized by a generalized NFW density profile. This contribution to the convergence from each halo is then projected onto the kappa map, appropriately weighted by the lensing kernel, W(z).
<gallery mode=packed heights="300px">
File:fullskykappa.png|"Halo only" full sky kappa map
File:flatskykappa.png|"Halo only" zoomed in kappa map
</gallery>

'''Field Model:''' (development in progress) Lagrangian perturbation theory is used to move matter not within halos to its final Eulerian position. This field matter distribution is then added directly to the kappa map. A [260 x 260 x 60] Mpc/h slab at z=0 is shown below with all the matter moved with either first (1LPT) or second (2LPT) order Lagrangian perturbation theory, in addition to the halos found with the peak patch method (these peak patch halos are also moved with either . Shown below are the same peak patch halo distributions, but also the

Documentation

2018-08-24T18:36:21Z

Gstein:

== Logbooks ==

Here are the logbooks from various WebSky related projects:

== Software ==

== Data ==

[http://cita.utoronto.ca/~malvarez/research/ksz-data/ Patchy kSZ Maps]

[https://www.dropbox.com/sh/deqz1gekpw0fapw/AABGKqqzG9wMnLP0LqFzE6i-a?dl=0/ Kappa, unlensed and lensed maps (LP)]

[https://www.dropbox.com/s/1evuw17snfqzhwj/all.pdf?dl=0 K, Phi, unlensed, lensed PDF (LP)]

== Transferring Data to Nersc ==

1.) log in to nersc - $ssh <username>@edison.nersc.gov

2.) log into edisongrid - $ssh edisongrid

3.) Sign into collaboration account
$module load globus
$myproxy-logon -s nerscca.nersc.gov !Password is the same as your username
$gsissh localhost -l cmbs4

4.) $cd /project/projectdirs/cmbs4

5.) ensure that the permissions are set appropriately (g+rX,o-rwx)

6.) Update README and cmbs4 wiki for any new files you add or changes you make!

README snippet for .npz catalogues
HALO CATALOGUES:
Contains (x,y,z,mass,redshift) for each halo. They are saved in the npz format of numpy (see http://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html). To load simply use
halo_cat = np.load(outfile)
xpk = halo_cat["xpk"]
ypk = halo_cat["ypk"]
zpk = halo_cat["zpk"]
M = halo_cat["M"]
redshift = halo_cat["redshift"]

README snippet for .pksc catalogues

Contains (x,y,z,v_x,v_y,v_z,R_TH) for each halo. The final halo catalog is a binary file with a 12 byte header, R_TH is the Lagrangian top hat radius of the halo. The conversion to mass is M_halo = 4*pi/3 * R_TH**3 * rho_bar, where rho_mean = 2.775e11*Omega_M*h**2.
header = (int Non, float RTHmax, float redshiftbox)
where Non is the total number of halos found,
RTHmax is the radius of the largest halo in the simulation,
redshiftbox is the redshift of the box, which will be negative for lightcone runs.
This 12 byte header is then followed by a list of Non*7 4 byte floats that represent (x_halo,y_halo,z_halo,vx_halo,vy_halo,vz_halo,Rth_halo)

Sample Python and Fortran code to load a datafile:

Python:
pkfile = open(filein,"rb")
Non = np.fromfile(pkfile, dtype=np.int32, count=1)
RTHMAXin = np.fromfile(pkfile, dtype=np.float32, count=1)
redshiftbox = np.fromfile(pkfile, dtype=np.float32, count=1)
print "Non = ", Non
npkdata = 7*Non
peakdata = np.fromfile(pkfile, dtype=np.float32, count=npkdata)
peakdata = np.reshape(peakdata,(Non,7))
xpos = peakdata[:,0]
ypos = peakdata[:,1]
zpos = peakdata[:,2]
vxpos = peakdata[:,3]
vypos = peakdata[:,4]
vzpos = peakdata[:,5]
Rth = peakdata[:,6]
Omega_M = 0.25
h = 0.7
rhomean = 2.775e11*Omega_M*h**2
M = 4.0/3*np.pi*Rth**3*rhomean

Redshifts
z=np.linspace(0,4,1000)
def hubble(z):
return h*100*np.sqrt(omegam*(1+z)**3+1-omegam)
def drdz(z):
return 3e5 / hubble(z)
r = np.cumsum(drdz(z)*(z[1]-z[0]))
r -= r[0]
z_to_r = sp.interpolate.interp1d(z,r)
r_to_z = sp.interpolate.interp1d(r,z)

Fortran:
open(4,file=filein,status='old',access='stream')
read(4) Npk,RTHLmax_in,boxredshift
allocate(posxyz(3,nhalo))
allocate(velxyz(3,nhalo))
allocate(rth(nhalo))
allocate(mass(nhalo))
allocate(vrad(nhalo))
offset_num_floats = 7*nhalo*myid
read(4) ((posxyz(j,i),j=1,3),&
(velxyz(j,i),j=1,3),&
rth(i),i=1,nhalo)
close(4)

== Modeling ==

=== CMB Lensing ===

Using a 'field+halo' model to generate lensing kappa maps from Gaussian random field (GRF) of density fluctuations on the past light cone.

'''Halo Model:''' The peak patch algorithm is run on the GRF to produce a catalog of halos with [RA, Dec, redshift, velocity, mass]. The halos are assumed to have a spherically-symmetric matter distribution parameterized by a generalized NFW density profile. This contribution to the convergence from each halo is then projected onto the kappa map, appropriately weighted by the lensing kernel, W(z).
<gallery mode=packed heights="300px">
File:fullskykappa.png|"Halo only" full sky kappa map
File:flatskykappa.png|"Halo only" zoomed in kappa map
</gallery>

'''Field Model:''' (development in progress) Lagrangian perturbation theory is used to move matter not within halos to its final Eulerian position. This field matter distribution is then added directly to the kappa map. A [260 x 260 x 60] Mpc/h slab at z=0 is shown below with all the matter moved with either first (1LPT) or second (2LPT) order Lagrangian perturbation theory, in addition to the halos found with the peak patch method (these peak patch halos are also moved with either . Shown below are the same peak patch halo distributions, but also the

MediaWiki:Mainpage

2018-01-08T04:38:59Z

Gstein:

CITA Extragalactic Mocks

MediaWiki:Mainpage

2018-01-08T03:02:51Z

Gstein:

The WebSky Suite of Extragalactic CMB Mocks

MediaWiki:Mainpage

2018-01-08T03:02:38Z

Gstein:

The WebSky Suite of Extragalactic Mocks

File:WebSky header.png

2018-01-06T02:25:17Z

Gstein: Gstein uploaded a new version of File:WebSky header.png

File:WebSky header.png

2018-01-06T02:19:56Z

Gstein:

Documentation

2017-12-15T17:13:55Z

Gstein:

<math>M_{200,M} = 2.62 \times 10^{13} M_\odot</math>

== Software ==

== Data ==

[http://cita.utoronto.ca/~malvarez/research/ksz-data/ Patchy kSZ Maps]

[https://www.dropbox.com/sh/deqz1gekpw0fapw/AABGKqqzG9wMnLP0LqFzE6i-a?dl=0/ Kappa, unlensed and lensed maps (LP)]

[https://www.dropbox.com/s/1evuw17snfqzhwj/all.pdf?dl=0 K, Phi, unlensed, lensed PDF (LP)]

== Transferring Data to Nersc ==

1.) log in to nersc - $ssh <username>@edison.nersc.gov

2.) log into edisongrid - $ssh edisongrid

3.) Sign into collaboration account
$module load globus
$myproxy-logon -s nerscca.nersc.gov !Password is the same as your username
$gsissh localhost -l cmbs4

4.) $cd /project/projectdirs/cmbs4

5.) ensure that the permissions are set appropriately (g+rX,o-rwx)

6.) Update README and cmbs4 wiki for any new files you add or changes you make!

README snippet for .npz catalogues
HALO CATALOGUES:
Contains (x,y,z,mass,redshift) for each halo. They are saved in the npz format of numpy (see http://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html). To load simply use
halo_cat = np.load(outfile)
xpk = halo_cat["xpk"]
ypk = halo_cat["ypk"]
zpk = halo_cat["zpk"]
M = halo_cat["M"]
redshift = halo_cat["redshift"]

README snippet for .pksc catalogues

Contains (x,y,z,v_x,v_y,v_z,R_TH) for each halo. The final halo catalog is a binary file with a 12 byte header, R_TH is the Lagrangian top hat radius of the halo. The conversion to mass is M_halo = 4*pi/3 * R_TH**3 * rho_bar, where rho_mean = 2.775e11*Omega_M*h**2.
header = (int Non, float RTHmax, float redshiftbox)
where Non is the total number of halos found,
RTHmax is the radius of the largest halo in the simulation,
redshiftbox is the redshift of the box, which will be negative for lightcone runs.
This 12 byte header is then followed by a list of Non*7 4 byte floats that represent (x_halo,y_halo,z_halo,vx_halo,vy_halo,vz_halo,Rth_halo)

Sample Python and Fortran code to load a datafile:

Python:
pkfile = open(filein,"rb")
Non = np.fromfile(pkfile, dtype=np.int32, count=1)
RTHMAXin = np.fromfile(pkfile, dtype=np.float32, count=1)
redshiftbox = np.fromfile(pkfile, dtype=np.float32, count=1)
print "Non = ", Non
npkdata = 7*Non
peakdata = np.fromfile(pkfile, dtype=np.float32, count=npkdata)
peakdata = np.reshape(peakdata,(Non,7))
xpos = peakdata[:,0]
ypos = peakdata[:,1]
zpos = peakdata[:,2]
vxpos = peakdata[:,3]
vypos = peakdata[:,4]
vzpos = peakdata[:,5]
Rth = peakdata[:,6]
Omega_M = 0.25
h = 0.7
rhomean = 2.775e11*Omega_M*h**2
M = 4.0/3*np.pi*Rth**3*rhomean

Redshifts
z=np.linspace(0,4,1000)
def hubble(z):
return h*100*np.sqrt(omegam*(1+z)**3+1-omegam)
def drdz(z):
return 3e5 / hubble(z)
r = np.cumsum(drdz(z)*(z[1]-z[0]))
r -= r[0]
z_to_r = sp.interpolate.interp1d(z,r)
r_to_z = sp.interpolate.interp1d(r,z)

Fortran:
open(4,file=filein,status='old',access='stream')
read(4) Npk,RTHLmax_in,boxredshift
allocate(posxyz(3,nhalo))
allocate(velxyz(3,nhalo))
allocate(rth(nhalo))
allocate(mass(nhalo))
allocate(vrad(nhalo))
offset_num_floats = 7*nhalo*myid
read(4) ((posxyz(j,i),j=1,3),&
(velxyz(j,i),j=1,3),&
rth(i),i=1,nhalo)
close(4)

== Modeling ==

=== CMB Lensing ===

Using a 'field+halo' model to generate lensing kappa maps from Gaussian random field (GRF) of density fluctuations on the past light cone.

'''Halo Model:''' The peak patch algorithm is run on the GRF to produce a catalog of halos with [RA, Dec, redshift, velocity, mass]. The halos are assumed to have a spherically-symmetric matter distribution parameterized by a generalized NFW density profile. This contribution to the convergence from each halo is then projected onto the kappa map, appropriately weighted by the lensing kernel, W(z).
<gallery mode=packed heights="300px">
File:fullskykappa.png|"Halo only" full sky kappa map
File:flatskykappa.png|"Halo only" zoomed in kappa map
</gallery>

'''Field Model:''' (development in progress) Lagrangian perturbation theory is used to move matter not within halos to its final Eulerian position. This field matter distribution is then added directly to the kappa map. A [260 x 260 x 60] Mpc/h slab at z=0 is shown below with all the matter moved with either first (1LPT) or second (2LPT) order Lagrangian perturbation theory, in addition to the halos found with the peak patch method (these peak patch halos are also moved with either . Shown below are the same peak patch halo distributions, but also the

Documentation

2017-12-15T17:12:55Z

Gstein:

<math>M_{200,M} = 2.62\ \times 10^{13} M_\odot</math>

== Software ==

== Data ==

[http://cita.utoronto.ca/~malvarez/research/ksz-data/ Patchy kSZ Maps]

[https://www.dropbox.com/sh/deqz1gekpw0fapw/AABGKqqzG9wMnLP0LqFzE6i-a?dl=0/ Kappa, unlensed and lensed maps (LP)]

[https://www.dropbox.com/s/1evuw17snfqzhwj/all.pdf?dl=0 K, Phi, unlensed, lensed PDF (LP)]

== Transferring Data to Nersc ==

1.) log in to nersc - $ssh <username>@edison.nersc.gov

2.) log into edisongrid - $ssh edisongrid

3.) Sign into collaboration account
$module load globus
$myproxy-logon -s nerscca.nersc.gov !Password is the same as your username
$gsissh localhost -l cmbs4

4.) $cd /project/projectdirs/cmbs4

5.) ensure that the permissions are set appropriately (g+rX,o-rwx)

6.) Update README and cmbs4 wiki for any new files you add or changes you make!

README snippet for .npz catalogues
HALO CATALOGUES:
Contains (x,y,z,mass,redshift) for each halo. They are saved in the npz format of numpy (see http://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html). To load simply use
halo_cat = np.load(outfile)
xpk = halo_cat["xpk"]
ypk = halo_cat["ypk"]
zpk = halo_cat["zpk"]
M = halo_cat["M"]
redshift = halo_cat["redshift"]

README snippet for .pksc catalogues

Contains (x,y,z,v_x,v_y,v_z,R_TH) for each halo. The final halo catalog is a binary file with a 12 byte header, R_TH is the Lagrangian top hat radius of the halo. The conversion to mass is M_halo = 4*pi/3 * R_TH**3 * rho_bar, where rho_mean = 2.775e11*Omega_M*h**2.
header = (int Non, float RTHmax, float redshiftbox)
where Non is the total number of halos found,
RTHmax is the radius of the largest halo in the simulation,
redshiftbox is the redshift of the box, which will be negative for lightcone runs.
This 12 byte header is then followed by a list of Non*7 4 byte floats that represent (x_halo,y_halo,z_halo,vx_halo,vy_halo,vz_halo,Rth_halo)

Sample Python and Fortran code to load a datafile:

Python:
pkfile = open(filein,"rb")
Non = np.fromfile(pkfile, dtype=np.int32, count=1)
RTHMAXin = np.fromfile(pkfile, dtype=np.float32, count=1)
redshiftbox = np.fromfile(pkfile, dtype=np.float32, count=1)
print "Non = ", Non
npkdata = 7*Non
peakdata = np.fromfile(pkfile, dtype=np.float32, count=npkdata)
peakdata = np.reshape(peakdata,(Non,7))
xpos = peakdata[:,0]
ypos = peakdata[:,1]
zpos = peakdata[:,2]
vxpos = peakdata[:,3]
vypos = peakdata[:,4]
vzpos = peakdata[:,5]
Rth = peakdata[:,6]
Omega_M = 0.25
h = 0.7
rhomean = 2.775e11*Omega_M*h**2
M = 4.0/3*np.pi*Rth**3*rhomean

Redshifts
z=np.linspace(0,4,1000)
def hubble(z):
return h*100*np.sqrt(omegam*(1+z)**3+1-omegam)
def drdz(z):
return 3e5 / hubble(z)
r = np.cumsum(drdz(z)*(z[1]-z[0]))
r -= r[0]
z_to_r = sp.interpolate.interp1d(z,r)
r_to_z = sp.interpolate.interp1d(r,z)

Fortran:
open(4,file=filein,status='old',access='stream')
read(4) Npk,RTHLmax_in,boxredshift
allocate(posxyz(3,nhalo))
allocate(velxyz(3,nhalo))
allocate(rth(nhalo))
allocate(mass(nhalo))
allocate(vrad(nhalo))
offset_num_floats = 7*nhalo*myid
read(4) ((posxyz(j,i),j=1,3),&
(velxyz(j,i),j=1,3),&
rth(i),i=1,nhalo)
close(4)

== Modeling ==

=== CMB Lensing ===

Using a 'field+halo' model to generate lensing kappa maps from Gaussian random field (GRF) of density fluctuations on the past light cone.

'''Halo Model:''' The peak patch algorithm is run on the GRF to produce a catalog of halos with [RA, Dec, redshift, velocity, mass]. The halos are assumed to have a spherically-symmetric matter distribution parameterized by a generalized NFW density profile. This contribution to the convergence from each halo is then projected onto the kappa map, appropriately weighted by the lensing kernel, W(z).
<gallery mode=packed heights="300px">
File:fullskykappa.png|"Halo only" full sky kappa map
File:flatskykappa.png|"Halo only" zoomed in kappa map
</gallery>

'''Field Model:''' (development in progress) Lagrangian perturbation theory is used to move matter not within halos to its final Eulerian position. This field matter distribution is then added directly to the kappa map. A [260 x 260 x 60] Mpc/h slab at z=0 is shown below with all the matter moved with either first (1LPT) or second (2LPT) order Lagrangian perturbation theory, in addition to the halos found with the peak patch method (these peak patch halos are also moved with either . Shown below are the same peak patch halo distributions, but also the

File:Fullsky z4pt5 tsz halos.png

2017-12-14T21:33:22Z

Gstein:

File:Fullsky z4pt5 ksz halos.png

2017-12-14T21:33:06Z

Gstein:

File:Fullsky z4pt5 kappa.png

2017-12-14T21:32:51Z

Gstein:

File:Fullsky z4pt5 cmb lensed.png

2017-12-14T21:32:35Z

Gstein:

File:Fullsky z4pt5 cmb diff.png

2017-12-14T21:32:16Z

Gstein:

File:Fullsky z4pt5 cib halos.png

2017-12-14T21:31:56Z

Gstein:

File:Fullsky z4pt5 cib 2lpt.png

2017-12-14T21:31:31Z

Gstein: