Dark Energy Survey (DES)

Note

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

This page explains how to measure galaxy-galaxy lensing using the DES Y3 shape catalog.

Downloading the Data

DES Y3 catalog data is publicly available here. Download the required files with:

BASE_URL=https://desdr-server.ncsa.illinois.edu/despublic/y3a2_files
wget $BASE_URL/y3kp_cats/{DESY3_sompz_v0.50.h5,DESY3_metacal_v03-004.h5,DESY3_indexcat.h5}
wget $BASE_URL/datavectors/2pt_NG_final_2ptunblind_02_26_21_wnz_maglim_covupdate.fits

Then run dsigma-process-des-y3 (see process_des_y3()) to process the raw files into a single des_y3.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 (Myles et al., 2021).

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

from dsigma.precompute import precompute

table_s = Table.read('des_y3.hdf5', path='catalog')
table_s['z_l_max'] = np.array(
    [0.0, 0.358, 0.631, 0.872])[table_s['z_bin']] - 0.1
table_n = Table.read('des_y3.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 the METACALIBRATION matrix shear response correction, a scalar shear response correction which accounts for blending, 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 DES.

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,
              matrix_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'des_{lens_bin}.csv', overwrite=True)

Acknowledgments

If you use DES Y3 data in your research, please follow the acknowledgment guidelines on the DES Y3 data release page.