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:

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 (simid, noiselevel), 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:

lsst_y10 — LSST Year 10 (DESC SRD)

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

Pipeline

1. 1.N-body simulations: Gower Street suite (791 simulations with varying cosmological parameters)
2. 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. 3.Shape noise injection: Gaussian noise added per pixel at nside=1024, calibrated to DES Y3 or LSST Y10 survey depth
4. 4.Harmonic filtering: map2alm(lmax) -> rotate_alm(euler) -> alm2map(nside_down) ensures all tiles see identical harmonic-space processing
5. 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.