Context. The Kilo-Degree Survey (KiDS) is an optical wide-field imaging survey carried out with the VLT Survey Telescope and the OmegaCAM camera. KiDS will image 1500 square degrees in four filters (ugri), and together with its near-infrared counterpart VIKING will produce deep photometry in nine bands. Designed for weak lensing shape and photometric redshift measurements, its core science driver is mapping the large-scale matter distribution in the Universe back to a redshift of ∼0.5. Secondary science cases include galaxy evolution, Milky Way structure, and the detection of high-redshift clusters and quasars. Aims. KiDS is an ESO Public Survey and dedicated to serving the astronomical community with high-quality data products derived from the survey data. Public data releases, the first two of which are presented here, are crucial for enabling independent confirmation of the survey's scientific value. The achieved data quality and initial scientific utilization are reviewed in order to validate the survey data. Methods. A dedicated pipeline and data management system based on A-WISE, combined with newly developed masking and source classification tools, is used for the production of the data products described here. Science projects based on these data products and preliminary results are outlined. Results. For 148 survey tiles (≈160 sq.deg.) stacked ugri images have been released, accompanied by weight maps, masks, source lists, and a multi-band source catalogue. Limiting magnitudes are typically 24.3, 25.1, 24.9, 23.8 (5σ in a 2 aperture) in ugri, respectively, and the typical r-band PSF size is less than 0.7 . The photometry prior to global homogenization is stable at the ∼2% (4%) level in gri (u) with some outliers due to non-photometric conditions, while the astrometry shows a typical 2D rms of 0.03 . Early scientific results include the detection of nine high-z QSOs, fifteen candidate strong gravitational lenses, high-quality photometric redshifts and structural parameters for hundreds of thousands of galaxies.

We present measurements of the radial gravitational acceleration around isolated galaxies, comparing the expected gravitational acceleration given the baryonic matter (gbar) with the observed gravitational acceleration (gobs), using weak lensing measurements from the fourth data release of the Kilo-Degree Survey (KiDS-1000). These measurements extend the radial acceleration relation (RAR), traditionally measured using galaxy rotation curves, by 2 decades in gobs into the low-acceleration regime beyond the outskirts of the observable galaxy. We compare our RAR measurements to the predictions of two modified gravity (MG) theories: modified Newtonian dynamics and Verlinde’s emergent gravity (EG). We find that the measured relation between gobs and gbar agrees well with the MG predictions. In addition, we find a difference of at least 6σ between the RARs of early- and late-type galaxies (split by Sérsic index and u − r colour) with the same stellar mass. Current MG theories involve a gravity modification that is independent of other galaxy properties, which would be unable to explain this behaviour, although the EG theory is still limited to spherically symmetric static mass models. The difference might be explained if only the early-type galaxies have significant (Mgas ≈ M⋆) circumgalactic gaseous haloes. The observed behaviour is also expected in Λ-cold dark matter (ΛCDM) models where the galaxy-to-halo mass relation depends on the galaxy formation history. We find that MICE, a ΛCDM simulation with hybrid halo occupation distribution modelling and abundance matching, reproduces the observed RAR but significantly differs from BAHAMAS, a hydrodynamical cosmological galaxy formation simulation. Our results are sensitive to the amount of circumgalactic gas; current observational constraints indicate that the resulting corrections are likely moderate. Measurements of the lensing RAR with future cosmological surveys (such as Euclid) will be able to further distinguish between MG and ΛCDM models if systematic uncertainties in the baryonic mass distribution around galaxies are reduced.

We have obtained structural parameters of about 340, 000 galaxies from the Kilo Degree Survey (KiDS) in 153 square degrees of data release 1, 2 and 3. We have performed a seeing convolved 2D single Sérsic fit to the galaxy images in the 4 photometric bands (u, g, r, i) observed by KiDS, by selecting high signal-to-noise ratio (S/N > 50) systems in every bands.We have classified galaxies as spheroids and disc-dominated by combining their spectral energy distribution properties and their Sérsic index. Using photometric redshifts derived from a machine learning technique, we have determined the evolution of the effective radius, R e and stellar mass, M ⋆ , versus redshift, for both mass complete samples of spheroids and disc-dominated galaxies up to z∼ 0.6.Our results show a significant evolution of the structural quantities at intermediate redshift for the massive spheroids (Log M * /M ⊙ > 11, Chabrier IMF), while almost no evolution has found for less massive ones (Log M * /M ⊙ < 11). On the other hand, disc dominated systems show a milder evolution in the less massive systems (Log M * /M ⊙ < 11) and possibly no evolution of the more massive systems. These trends are generally consistent with predictions from hydrodynamical simulations and independent datasets out to redshift z ∼ 0.6, although in some cases the scatter of the data is large to drive final conclusions.These results, based on 1/10 of the expected KiDS area, reinforce precedent finding based on smaller statistical samples and show the route toward more accurate results, expected with the the next survey releases.

We report new high-quality galaxy-scale strong lens candidates found in the Kilo-Degree Survey data release 4 using machine learning. We have developed a new convolutional neural network (CNN) classifier to search for gravitational arcs, following the prescription by Petrillo et al. and using only r-band images. We have applied the CNN to two "predictive samples": a luminous red galaxy (LRG) and a "bright galaxy" (BG) sample (r<21). We have found 286 new high-probability candidates, 133 from the LRG sample and 153 from the BG sample. We have ranked these candidates based on a value that combines the CNN likelihood of being a lens and the human score resulting from visual inspection (P-value), and here we present the highest 82 ranked candidates with P-values 0.5. All of these high-quality candidates have obvious arc or pointlike features around the central red defector. Moreover, we define the best 26 objects, all with P-values 0.7, as a "golden sample" of candidates. This sample is expected to contain very few false positives; thus, it is suitable for follow-up observations. The new lens candidates come partially from the more extended footprint adopted here with respect to the previous analyses and partially from a larger predictive sample (also including the BG sample). These results show that machine-learning tools are very promising for finding strong lenses in large surveys and more candidates can be found by enlarging the predictive samples beyond the standard assumption of LRGs. In the future, we plan to apply our CNN to the data from next-generation surveys such as the Large Synoptic Survey Telescope, Euclid, and the Chinese Space Station Optical Survey.