English

The third data release of the Kilo-Degree Survey and associated data products

Astrophysics of Galaxies 2017-08-30 v2 Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics

Abstract

The Kilo-Degree Survey (KiDS) is an ongoing optical wide-field imaging survey with the OmegaCAM camera at the VLT Survey Telescope. It aims to image 1500 square degrees in four filters (ugri). The core science driver is mapping the large-scale matter distribution in the Universe, using weak lensing shear and photometric redshift measurements. Further science cases include galaxy evolution, Milky Way structure, detection of high-redshift clusters, and finding rare sources such as strong lenses and quasars. Here we present the third public data release (DR3) and several associated data products, adding further area, homogenized photometric calibration, photometric redshifts and weak lensing shear measurements to the first two releases. A dedicated pipeline embedded in the Astro-WISE information system is used for the production of the main release. Modifications with respect to earlier releases are described in detail. Photometric redshifts have been derived using both Bayesian template fitting, and machine-learning techniques. For the weak lensing measurements, optimized procedures based on the THELI data reduction and lensfit shear measurement packages are used. In DR3 stacked ugri images, weight maps, masks, and source lists for 292 new survey tiles (~300 sq.deg) are made available. The multi-band catalogue, including homogenized photometry and photometric redshifts, covers the combined DR1, DR2 and DR3 footprint of 440 survey tiles (447 sq.deg). Limiting magnitudes are typically 24.3, 25.1, 24.9, 23.8 (5 sigma in a 2 arcsec aperture) in ugri, respectively, and the typical r-band PSF size is less than 0.7 arcsec. The photometric homogenization scheme ensures accurate colors and an absolute calibration stable to ~2% for gri and ~3% in u. Separately released are a weak lensing shear catalogue and photometric redshifts based on two different machine-learning techniques.

Keywords

Cite

@article{arxiv.1703.02991,
  title  = {The third data release of the Kilo-Degree Survey and associated data products},
  author = {J. T. A. de Jong and G. A. Verdoes Kleijn and T. Erben and H. Hildebrandt and K. Kuijken and G. Sikkema and M. Brescia and M. Bilicki and N. R. Napolitano and V. Amaro and K. G. Begeman and D. R. Boxhoorn and H. Buddelmeijer and S. Cavuoti and F. Getman and A. Grado and E. Helmich and Z. Huang and N. Irisarri and F. La Barbera and G. Longo and J. P. McFarland and R. Nakajima and M. Paolillo and E. Puddu and M. Radovich and A. Rifatto and C. Tortora and E. A. Valentijn and C. Vellucci and W-J. Vriend and A. Amon and C. Blake and A. Choi and I. Fenech Conti and R. Herbonnet and C. Heymans and H. Hoekstra and D. Klaes and J. Merten and L. Miller and P. Schneider and M. Viola},
  journal= {arXiv preprint arXiv:1703.02991},
  year   = {2017}
}

Comments

small modifications; 27 pages, 12 figures, accepted for publication in Astronomy & Astrophysics