EiffL/GowerStreetDESY3

Gower Street DES Y3 Lensing Tiles

Weak lensing convergence map tiles extracted from the Gower Street N-body simulation suite, processed through a Born-approximation raytracing pipeline with DES Y3 MagLim source n(z) distributions.

Dataset Description

Each sample contains a (4, H, W) convergence map tile covering ~3400 deg², corresponding to 4 DES Y3 MagLim tomographic bins. Tiles are extracted from equatorial HEALPix base faces after harmonic-space filtering and rotation for data augmentation.

The dataset includes 15 configurations (5 angular scale cuts x 3 noise levels), with ~9400 tiles per configuration from 787 simulations (12 tiles per sim: 3 rotations x 4 equatorial tiles).

Configurations

Each configuration is identified by lmax_{lmax}_{noise_level} and stored in a separate data directory:

lmax	Tile size	Angular scales	nside	Noise levels
200	128x128	> 0.9 deg	128	noiseless, des_y3, lsst_y10
400	256x256	> 0.45 deg	256	noiseless, des_y3, lsst_y10
600	256x256	> 0.3 deg	256	noiseless, des_y3, lsst_y10
800	512x512	> 0.23 deg	512	noiseless, des_y3, lsst_y10
1000	512x512	> 0.18 deg	512	noiseless, des_y3, lsst_y10

Noise levels

Shape noise arises from the intrinsic ellipticity dispersion of source galaxies. For a HEALPix pixel at resolution nside, the noise standard deviation per pixel per tomographic bin is:

sigma_pix = sigma_e / sqrt(2 * n_eff * A_pix)

where sigma_e is the per-component intrinsic ellipticity dispersion, n_eff is the effective galaxy number density (in sr⁻¹), and A_pix = 4pi / N_pix is the pixel solid angle. The factor of 2 accounts for two ellipticity components. Noise is Gaussian and independent per pixel.

Shape noise is added to the full-sky nside=1024 convergence map before harmonic filtering, so the noise is band-limited consistently with the signal. For a given (sim_id, noise_level), the same noise realization is shared across all lmax cuts and orientations. RNG seed: sim_id * 1000 + noise_level_index.

noiseless

No shape noise added. Pure signal from the Born-approximation raytracing.

des_y3 — DES Year 3 (Amon et al. 2022, Table 1)

Per-bin effective number density and intrinsic ellipticity dispersion from the DES Y3 MagLim sample:

Bin	n_eff (arcmin⁻²)	sigma_e
0	1.476	0.243
1	1.479	0.262
2	1.484	0.259
3	1.461	0.301

lsst_y10 — LSST Year 10 (DESC SRD)

Bin	n_eff (arcmin⁻²)	sigma_e
0-3	6.75	0.26

Total n_eff = 27 arcmin⁻² split uniformly across 4 bins to match the DES tomographic structure.

Loading

from datasets import load_dataset

# Load a specific (lmax, noise_level) configuration
ds = load_dataset("EiffL/GowerStreetDESY3", data_dir="data/lmax_600_des_y3")

sample = ds["train"][0]
kappa = sample["kappa"]           # (4, 256, 256) convergence map
omega_m = sample["Omega_m"]       # Matter density parameter
noise = sample["noise_level"]     # "des_y3"

# Load noiseless version at same angular scale
ds_clean = load_dataset("EiffL/GowerStreetDESY3", data_dir="data/lmax_600_noiseless")

# Load LSST-depth version
ds_lsst = load_dataset("EiffL/GowerStreetDESY3", data_dir="data/lmax_600_lsst_y10")

Fields

Field	Type	Description
kappa	array (4, H, W) float32	Convergence map tiles, 4 tomographic bins
sim_id	int	Gower Street simulation ID (1-791)
orientation_id	int	Rotation orientation (0-2)
tile_id	int	Equatorial tile index (0-3)
noise_level	string	Noise level: "noiseless", "des_y3", or "lsst_y10"
Omega_m	float	Matter density parameter
sigma_8	float	RMS density fluctuation amplitude
S8	float	S8 = sigma_8 * sqrt(Omega_m / 0.3)
w	float	Dark energy equation of state
h	float	Hubble parameter H0/100
n_s	float	Scalar spectral index
Omega_b	float	Baryon density parameter
m_nu	float	Sum of neutrino masses (eV)

Pipeline

1. N-body simulations: Gower Street suite (791 simulations with varying cosmological parameters)
2. Raytracing: Born-approximation lensing through particle lightcone shells (nside=2048 input, nside=1024 output), weighted by DES Y3 MagLim n(z) distributions (4 tomographic bins)
3. Shape noise injection: Gaussian noise added per pixel at nside=1024, calibrated to DES Y3 or LSST Y10 survey depth
4. Harmonic filtering: map2alm(lmax) -> rotate_alm(euler) -> alm2map(nside_down) ensures all tiles see identical harmonic-space processing
5. Tile extraction: 3 fixed rotations x 4 equatorial HEALPix base tiles = 12 tiles per simulation per configuration

Rotations

Three orientations of the sphere provide data augmentation while keeping tiles in equatorial positions (minimal projection distortion):

• Orientation 0: identity (Euler angles 0, 0, 0)
• Orientation 1: 90 deg about y-axis (0, 90, 0)
• Orientation 2: 90 deg about z-axis (90, 0, 0)

Source

• Simulations: Gower Street (Jeffrey et al.)
• n(z): DES Y3 MagLim
• Pipeline: LensingFoM

Citation

If you use this dataset, please cite the Gower Street simulations paper and DES Y3 data release.