A pipeline designed to generate multi-band images and create joint redshift-shear mock catalogues.
It contains 8 modules labelled as
For usage instructions, run
`python modules/Run.py --help`For sample test scripts, refer to the scripts directory.
SKiLLS: SURFS-based KiDS-Legacy-Like Simulations
The post-processing codes can be found in the post_processing directory.
The catalogues can be download here
NOTE: These catalogues include all objects detected by SExtractor, encompassing false detections and stars. However, they also feature flag columns for applying KiDS-like photometry and lensfit selections. For more details, please refer to the readme.txt file within the shared folder.
The raw measurements from lensfit exhibit biases, primarily resulting from PSF anisotropy. We address these PSF-related distortions using an empirical approach, as elaborated in Section 4 of Li et al. (2023).
The relevant codes can be found in the alphaRecalPlus repository.
After generating the images, we discovered that our SURFS-based galaxies possess photometry derived from SDSS filters, which differ slightly from the KiDS/VIKING filters. Consequently, we applied an empirical correction to the photometry measured from the simulated images to account for the filter discrepancies.
A comprehensive explanation is available in Appendix C of Li et al. (2023), and the pertinent codes can be found in the change_filters repository.
Our shear calibration contains two main steps:
Estimate shear biases using constant shear image simulations, adhering to previous KiDS conventions, and apply data reweighting based on the lensfit-reported model signal-to-noise ratio and object resolution to accommodate potential discrepancies between simulations and data. Further information is provided in Section 5.1 of Li et al. (2023), with the associated codes located in the biasEstimation repository.
Refine the shear biases from constant shear image simulations to address high-order effects stemming from the 'shear-interplay' effect and PSF modeling uncertainties. Further information can be found in Sections 5.2 and 5.3 of Li et al. (2023), with the corresponding codes located in the correction_varShear_PSFmodelling repository.
Should the code or catalogues prove beneficial to your projects, please consider citing our paper: Li et al. 2023.