Photometric redshifts for the Kilo-Degree Survey

Abstract

We present a machine-learning photometric redshift (ML photo-z) analysis of the Kilo-Degree Survey Data Release 3 (KiDS DR3), using two neural-network based techniques: ANNz2 and MLPQNA. Despite limited coverage of spectroscopic training sets, these ML codes provide photo-zs of quality comparable to, if not better than, those from the Bayesian Photometric Redshift (BPZ) code, at least up to zphot ≲ 0.9 and r ≲ 23.5. At the bright end of r ≲ 20, where very complete spectroscopic data overlapping with KiDS are available, the performance of the ML photo-zs clearly surpasses that of BPZ, currently the primary photo-z method for KiDS. Using the Galaxy And Mass Assembly (GAMA) spectroscopic survey as calibration, we furthermore study how photo-zs improve for bright sources when photometric parameters additional to magnitudes are included in the photo-z derivation, as well as when VIKING and WISE infrared (IR) bands are added. While the fiducial four-band ugri setup gives a photo-z bias 〈δz/(1 + z)〉 = −2 × 10−4 and scatter σδz/(1+z) < 0.022 at mean 〈z〉 = 0.23, combining magnitudes, colours, and galaxy sizes reduces the scatter by ~7% and the bias by an order of magnitude. Once the ugri and IR magnitudes are joined into 12-band photometry spanning up to 12 μm, the scatter decreases by more than 10% over the fiducial case. Finally, using the 12 bands together with optical colours and linear sizes gives 〈δz/(1 + z)〉 < 4 × 10−5 and σδz/(1+z) < 0.019. This paper also serves as a reference for two public photo-z catalogues accompanying KiDS DR3, both obtained using the ANNz2 code. The first one, of general purpose, includes all the 39 million KiDS sources with four-band ugri measurements in DR3. The second dataset, optimised for low-redshift studies such as galaxy-galaxy lensing, is limited to r ≲ 20, and provides photo-zs of much better quality than in the full-depth case thanks to incorporating optical magnitudes, colours, and sizes in the GAMA-calibrated photo-z derivation.

Catalog of quasars from the Kilo-Degree Survey Data Release 3

“…An additional potentially useful feature in the classification process could be photometric redshifts (photo-zs). However, KiDS DR3 photo-zs were optimized for galaxies (de Jong et al 2017;Bilicki et al 2018) and are unreliable for quasars, as we indeed verified for overlapping spectroscopic QSOs. In future work we will address the issue of deriving more robust QSO photo-zs in KiDS as well as estimating them jointly with object classification (see e.g., Yèche et al 2010).…”

Luminous red galaxies in the Kilo-Degree Survey: selection with broad-band photometry and weak lensing measurements

“…For the selected galaxies, we also assess the quality of the estimated red-sequence redshifts by comparing them with other photo-z estimation methods available in KiDS DR3. These include template-fitting BPZ photo-z's (Benítez 2000), as well as those determined by the machine learning method ANNz2 (Sadeh et al 2016), as described in de Jong et al (2017) and Bilicki et al (2018). Those photo-z's are available for all galaxy types, but here we will discuss their performance only for the LRGs contained in our samples.…”

“…For the machine-learning results, we make use of two estimates of ANNz2 photo-z's presented in Bilicki et al (2018). The first set of redshifts, which we call the bright ANNz2 photo-z's, are the photo-z's that are exclusively trained on GAMA, and their performance is enhanced by using not only magnitudes, but also colours and angular sizes in the feature space.…”

“…The first set of redshifts, which we call the bright ANNz2 photo-z's, are the photo-z's that are exclusively trained on GAMA, and their performance is enhanced by using not only magnitudes, but also colours and angular sizes in the feature space. After a posteriori cut to the apparent magnitude (m r,AUTO < 20.3), Bilicki et al (2018) demonstrated that compared to the GAMA spectroscopic redshifts, the bright ANNz2 photo-z's have a scatter of 0.026 and a mean bias of −3.3 × 10 −3 . Note that as a result of the magnitude cut, the maximum redshift in the bright ANNz2 catalogue is approximately 0.62.…”

“…For those photo-z's only GAaP magnitudes were used as features, but weighting was applied to the training data to mimic the magnitude distribution in the target photometric sample. See Bilicki et al (2018) for details. Figure 5 compares the bias and the scatter as a function of photometric redshifts for the dense sample derived by Figure 1.…”