Columbia Lensing

Welcome!
We are the weak lensing group originated from Columbia University Department of Astronomy.
Here you can download weak lensing maps we created using the XSEDE computing clusters.
(Background image credit: CFHTLenS)


About

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.

Active contributors:

Zoltan Haiman
(Columbia)
Jia Liu
(Princeton)
Jose M. Zorrilla
(Columbia)
J. Colin Hill
(IAS/Flatiron)
Simeon Bird
(UC Riverside)
Mathew Madhavacheril
(Princeton)
Andrea Petri
(Columbia)
David Spergel
(Princeton/Flatiron)
Past contributors:
Morgan May, Jan Kratochvil, Xiuyuan Yang

PUBLICATIONS

Non-Gaussian information from weak lensing data via deep learning
A. Gupta, J. M. Zorrilla, D. Hsu & Z. Haiman
Accepted to Physical Review D (2018)
MassiveNuS: Cosmological Massive Neutrino Simulations
J. Liu, S. Bird, J. M. Zorrilla, J. C. Hill, Z. Haiman, M. Madhavacheril, A. Petri & D. N. Spergel
Journal of Cosmology and Astroparticle Physics, Issue 03, article id. 049 (2018)
Do dark matter halos explain lensing peaks?
J. M. Zorrilla, Z. Haiman, D. Hsu, A. Gupta & A. Petri
Physical Review D, Volume 94, Issue 8, id.083506 (2016)
CMB Lensing Beyond the Power Spectrum: Cosmological Constraints from the One-Point PDF and Peak Counts
J. Liu, J. C. Hill, B. D. Sherwin, A. Petri, V. Böhm & Z. Haiman
Physical Review D, Volume 94, Issue 10, id.103501 (2016)
Mocking the Weak Lensing universe: the LensTools python computing package
A. Petri
Astronomy and Computing, Volume 17 (2016)
Origin of Weak Lensing Convergence Peaks
J. Liu & Z. Haiman
Physical Review D, vol. 94, Issue 4, id.043533 (2016)
Cosmology with photometric weak lensing surveys: constraints with redshift tomography of convergence peaks and moments
A. Petri, M. May & Z. Haiman
Accepted to appear in Physical Review D, submitted May (2016)
Constraining multiplicative bias in CFHTLenS weak lensing shear data
J. Liu, A. Ortiz-Vazquez & J. C. Hill
Physical Review D, Volume 93, Issue 10, id.103508 (2016)
Sample variance in weak lensing: how many simulations are required?
A. Petri, Z. Haiman & M. May
Physical Review D, vol. 93, Issue 6, id.063524 (2016)
Cross-correlation of Planck CMB lensing and CFHTLenS galaxy weak lensing maps
J. Liu & J. C. Hill,
hysical Review D, Volume 92, Issue 6, id.063517 (2015)
Emulating the CFHTLenS Weak Lensing data: Cosmological Constraints from moments and Minkowski functionals
A. Petri, J. Liu, Z. Haiman, M. May, L. Hui & J. M. Kratochvil,
Physical Review D, vol. 91, Issue 10, id. 103511 (2015)
Cosmology Constraints from the Weak Lensing Peak Counts and the Power Spectrum in CFHTLenS
J. Liu, A. Petri, Z. Haiman, L. Hui, J. M. Kratochvil & M. May
Physical Review D, vol. 91, Issue 6, id. 063507 (2015)
The impact of spurious shear on cosmological parameter estimates from weak lensing observables
A. Petri, M. May, Z. Haiman, J. M. Kratochvil
Physical Review D, vol. 90, Issue 12, id. 123015 (2014)
The Impact of Magnification and Size Bias on Weak Lensing Power Spectrum and Peak Statistics
J. Liu, Z. Haiman, L. Hui, J. M. Kratochvil & M. May
Physical Review D, vol. 89, Issue 1, id. 023515 (2014)
Cosmology with Minkowski functionals and moments of the weak lensing convergence field
A. Petri, Z. Haiman, L. Hui, M. May & J. M. Kratochvil
Physical Review D, vol. 88, Issue 12, id. 123002 (2013)
Effect of Measurement Errors on Predicted Cosmological Constraints from Shear Peak Statistics with LSST
D. Bard, J. M. Kratochvil et al.
The Astrophysical Journal, vol. 774, article id. 49, 13 pp. (2013)
Baryon impact on weak lensing peaks and power spectrum: low-bias statistics and self-calibration in future surveys
X. Yang, J. M. Kratochvil, K. Huffenberger, Z. Haiman, & M. May
Physical Review D, vol. 87, Issue 2, id. 023511 (2013)
Probing Cosmology with Weak Lensing Minkowski Functionals
J. M. Kratochvil, E. A. Lim, S. Wang, Z. Haiman, M. May & K. Huffenberger
Physical Review D, vol. 85, issue 10, id. 103513 (2012)
Cosmological Information in Weak Lensing Peaks
X. Yang, J. M. Kratochvil, S. Wang, E. A. Lim, Z. Haiman & M. May
Physical Review D, vol. 84, issue 4, id. 043529 (2011)
Probing Cosmology with Weak Lensing Peak Counts
J. M. Kratochvil, Z. Haiman & M. May
Physical Review D, vol. 81, issue 4, id. 043519 (2010)
Constraining Cosmology with High Convergence Regions in Weak Lensing Surveys
S. Wang, Z. Haiman & M. May
The Astrophysical Journal, vol. 691, pp. 547-559 (2009)

MassiveNuS

DOWNLOAD
data
Please contact Jia Liu (jia@astro.princeton.edu) for technical issues.

DESCRIPTION (simulation paper: arxiv:1711.10524):
MassiveNuS (Cosmological Massive Neutrino Simulations) include 100 massive neutrino models + 1 massless model with three varying parameters (parameter file):
(1) neutrino mass sum M_nu (ranging from 0 to 0.6 eV, assuming normal hierarchy)
(2) matter density Omega_m
(3) primordial power spectrum amplitude A_s (sigma_8 is a derived parameter)

The simulations correctly capture the background expansion as neutrinos turn from relativistic to non-relativistic, as well as the growth of neutrino clustering in response to the nonlinear matter growth.

DATA PRODUCTS (data)
(1) 67 snapshots:
For the two fiducial models (z=0 to z=45, every126 comoving Mpc/h)
Format: Gadget-2 format2, with position and velocity information.
Size: 2.2TB/model.
Code: Gadget-2 (1024^3 particles, 512 Mpc/h box size) + neutrino patch (Ali-Haïmoud & Bird 2013)
(2) Halo catalogues:
For each of the 101 models.
Halos and properties, complete down to minimal halo mass 10^11.5 M_sun, around three million halos at z=0, for z=0 to z=45.
Format: ascii (recommend analysis tool: Halotools)
Size: 16GB/model.
Code: rockstar
(3) Merger trees:
For the two fiducial models.
Format: ascii (recommend analysis tool: Halotools)
Size: 18GB/model. Code: consistent tree
(4) Convergence maps (galaxy & CMB lensing):
10,000 realizations for each of the 101 models, for 6 source redshifts.
Source redshifts z_s = 0.5, 1, 1.5, 2, 2.5, 1100.
Map size: 12.25 deg^2, 512^2 pixels, 0.4 arcmin resolution.
Format: fits.
Size: 59GB/model (10,000 realizations x 6 redshifts).
Code: LensTools


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.

Dark Energy

DESCRIPTION
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:
"Om0.260_Ode0.740_w-0.800_wa0.000_si0.800.tar.gz" has:
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

kappa maps:
Om0.230_Ode0.770_w-1.000_wa0.000_si0.800.tar.gz
Om0.260_Ode0.740_w-0.800_wa0.000_si0.800.tar.gz
Om0.260_Ode0.740_w-1.000_wa-0.200_si0.800.tar.gz
Om0.260_Ode0.740_w-1.000_wa-0.500_si0.800.tar.gz
Om0.260_Ode0.740_w-1.000_wa0.000_si0.800.tar.gz
Om0.260_Ode0.740_w-1.200_wa0.000_si0.800.tar.gz
Om0.290_Ode0.710_w-1.000_wa0.000_si0.800.tar.gz

B-mode maps (for null tests):
Om0.230_Ode0.770_w-1.000_wa0.000_si0.800.tar.gz
Om0.260_Ode0.740_w-0.800_wa0.000_si0.800.tar.gz
Om0.260_Ode0.740_w-1.000_wa-0.200_si0.800.tar.gz
Om0.260_Ode0.740_w-1.000_wa-0.500_si0.800.tar.gz
Om0.260_Ode0.740_w-1.000_wa0.000_si0.800.tar.gz
Om0.260_Ode0.740_w-1.200_wa0.000_si0.800.tar.gz
Om0.290_Ode0.710_w-1.000_wa0.000_si0.800.tar.gz

Dark Matter

DESCRIPTION
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 zoltan@astro.columbia.edu 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

Acknowledgement

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.