Published March 31, 2026 | Version v1
Dataset Open

The Strong Lensing Discovery Engine F – Bright and low-redshift strong lenses

Authors/Creators

  • 1. ROR icon Max Planck Institute for Extraterrestrial Physics
  • 2. ROR icon Ludwig-Maximilians-Universität München
  • 3. ROR icon University of Portsmouth
  • 4. ROR icon University of Oxford

Description

This repository shares catalogues, images, and modelling results for strong lens candidates found in the Euclid Q1 data release as part of the Strong Lensing Discovery Engine F. Lenses were found through a combined machine learning, citizen science, and expert visual inspection campaign. The sample is described and presented in the following paper. 

It extends these Euclid Q1 discovery engine paper series:
A: System overview and lens catalogue

B: Early strong lens candidates from visual inspection of high velocity dispersion galaxies

C: Finding lenses with machine learning

D: Double-source-plane lens candidates

E: Ensemble classification of strong gravitational lenses: lessons for Data Release 1

F: Bright and low redshift strong lenses

If you use Euclid Q1 data in your research, please also cite the core Euclid Q1 papers.

Euclid is a space telescope surveying ~14k square degrees over the next six years. Q1 is the first major data release, covering 63 square degrees in the Euclid Deep Fields (see the paper). It is representative of the ongoing Euclid Wide Survey. Euclid images include a VIS optical image (400-900nm) and 3 NIR images (Y, J, H). The Euclid VIS PSF FWHM is approx. 0.16 arcsec per pixel.

We are continuing to search for strong lenses in the ongoing survey. We expect around 10k candidates in Euclid DR1. 

Table of contents

Products

The list of strong lens candidates is lens_table.csv. These are 72 candidates and are reported in our paper. All have following columns:

ID IAU name
RA_deg right ascension in degrees of host galaxy
Dec_deg declination in degrees of host galaxy
z_phot photometric redshift 
z_spec spectroscopic redshift if known
VI_score numerical score of candidate where higher indicates more likely to be a lens
mag_IE brightness of the host galaxy in magnitudes
n_sersic sersic index of host galaxy
log10_Mstar stellar mass of host galaxy using Chabrier IMF
theta_E_arcsec einstein radius in arcsec
comments additional comments by first three authors

 

The folder SLDE_F_zenodo.zip includes folders of succesful strong lens models. We attempted to fit lens models to all candidates rated 1.5 and above (Grade B or better). Modeling was performed by fitting the VIS image as the "primary" wavelength image and then fixing its mass model to reconstruct the source in the NIR images. For unreasonable NIR source only models and reconstructions of the VIS are shown in the png files. If source reconstruction of the NIR images and the arcs are needed, or any additional information, please request the author. 

The following lens model is used:

- A Multi Gaussian Expansion (MGE) lens light model (see He et al. 2024).
- An SIE with external shear mass model.
- A source galaxy reconstructed using an adaptive Voronoi mesh (see PyAutoLens docs).

For more information on the modeling procedure, please contact the author if the information is not already given here SLDE A-F.

 

Following files are included:

{tile_id}_{ra}_{dec}.fits

data of candidate in following structure

1.VIS 2. VIS_PSF 3. VIS_rms 4. NIR_Y 5. NIR_Y_PSF 6. NIR_Y_rms 7. NIR_J 8. NIR_J_PSF 9. NIR_J_rms 10. NIR_H 11. NIR_H_PSF 12. NIR_H_rms
lens_light.fits fits file of MGE model of host galaxy
mask_extra_galaxies.fits fits file of mask
model.results parameter results of strong lens model
result_lens_mass.json condensed parameter results of strong lens model
sie_fit_pix.png images of lens model either in only VIS band or in all 4 bands.
source_light.fits fits file of lensed arc
subplot_fit.png images of lens model with residuals only in VIS
subplot_fit_log10.png same as above with log10 scaling

 

Files

lens_table.csv

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