Abstract

Kilo-Degree Survey (KiDS), thanks to its depth, excellent imaging and multi-wavelength coverage (jointly with VIKING ), allows us to go beyond cosmic shear analyses. I will present how we extract and employ both low- and high-redshift galaxies and quasars from KiDS, taking advantage of its considerable overlap with various spectroscopic calibration datasets, such as GAMA and now also DESI . In selecting these objects and estimating their photometric redshifts, we use in particular machine-learning approaches including deep learning, and I will discuss our recent results in this matter. I will then overview some of the recent applications of these KiDS photometric galaxies and quasars, based on probes such as galaxy-galaxy lensing and clustering. Time permitting, I will also sketch near-future prospects of extending this kind of studies beyond KiDS with new surveys such as 4MOST and LSST .