Kilo-Degree Survey (KiDS)

Note

This is an unofficial guide to using KiDS data with dsigma. It has not been reviewed by the KiDS collaboration. For questions about the data products themselves, refer to the official KiDS documentation.

This page explains how to measure galaxy-galaxy lensing using the KiDS-Legacy shape catalog.

Downloading the Data

KiDS-Legacy catalogs are publicly available here. Download the required files with:

BASE_URL=https://kids.strw.leidenuniv.nl/DR5/data_files
wget $BASE_URL/{KiDS_Legacy_NS_unblind_final.fits.gz,KiDZ_Legacy_unblind_final.fits}

Then run dsigma-process-kids-legacy (see process_kids_legacy()) to process the raw files into a single kids_legacy.hdf5 file used in the steps below.

Precomputing the Signal

We apply a lens-source separation cut of \(z_l < z_{t, \rm low} - 0.1\), where \(z_{t, \rm low}\) is the lower edge of the tomographic bin each source belongs to (Wright et al., 2026).

import numpy as np
from astropy.cosmology import Planck15
from astropy.table import Table

from dsigma.precompute import precompute

table_s = Table.read('kids_legacy.hdf5', path='catalog')
table_s['z_l_max'] = np.array(
    [0.1, 0.3, 0.5, 0.7, 0.9])[table_s['z_bin']] - 0.1
table_n = Table.read('kids_legacy.hdf5', path='calibration')

rp_bins = np.logspace(-1, 1.6, 14)
kwargs = dict(cosmology=Planck15, comoving=True, table_n=table_n,
              progress_bar=True)
precompute(table_l, table_s, rp_bins, **kwargs)
precompute(table_r, table_s, rp_bins, **kwargs)

Stacking the Signal

We stack the signal in four BOSS redshift bins. Lenses and randoms with no nearby source galaxies are removed first. Jackknife resampling with 100 fields is used to estimate uncertainties.

We apply a scalar shear response correction and subtract the signal around randoms. Random subtraction removes additive systematics, reduces noise, and is strongly recommended. We do not apply a boost correction, as our estimator may be biased for KiDS.

import numpy as np

from dsigma.jackknife import compute_jackknife_fields, jackknife_resampling
from dsigma.stacking import excess_surface_density

# Drop all lenses and randoms that did not have any nearby source.
table_l = table_l[np.sum(table_l['sum 1'], axis=1) > 0]
table_r = table_r[np.sum(table_r['sum 1'], axis=1) > 0]

centers = compute_jackknife_fields(
    table_l, 100, weights=np.sum(table_l['sum 1'], axis=1))
compute_jackknife_fields(table_r, centers)

kwargs = dict(scalar_shear_response_correction=True,
              random_subtraction=True)
z_bins = np.array([0.15, 0.31, 0.43, 0.54, 0.70])

for lens_bin, (z_min, z_max) in enumerate(zip(z_bins[:-1], z_bins[1:])):
    table_l_bin = table_l[(z_min <= table_l['z']) & (table_l['z'] < z_max)]
    table_r_bin = table_r[(z_min <= table_r['z']) & (table_r['z'] < z_max)]

    result = excess_surface_density(
        table_l_bin, table_r=table_r_bin, return_table=True, **kwargs)
    result['ds_err'] = np.sqrt(np.diag(jackknife_resampling(
        excess_surface_density, table_l_bin, table_r=table_r_bin, **kwargs)))

    result.write(f'kids_{lens_bin}.csv', overwrite=True)

Acknowledgments

If you use KiDS-Legacy data in your research, please follow the KiDS-Legacy acknowledgment guidelines.