Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning

Abstract

We employ deep learning (DL) to improve photometric redshifts (photo-$z$s) in the Kilo-Degree Survey Data Release 4 Bright galaxy sample (KiDS-Bright DR4). This dataset, used as a foreground for KiDS lensing and clustering studies, is flux-limited to $r<20$ mag with mean $z=0.23$ and covers 1000 deg${}^2$. Its photo-$z$s were previously derived with artificial neural networks from the ANNz2 package, trained on the Galaxy And Mass Assembly (GAMA) spectroscopy. Here we considerably improve over these previous redshift estimations by building a DL model, Hybrid-z, which combines four-band KiDS images with nine-band magnitudes from KiDS+VIKING. The Hybrid-z framework provides photo-$z$s for KiDS-Bright, with negligible mean residuals of O($10^{-4}$) and scatter at the level of $0.014(1+z)$ -- reduction by 20% over the previous nine-band derivations with ANNz2. We check our photo-$z$ model performance on test data drawn from GAMA, as well as from other KiDS-overlapping wide-angle spectroscopic surveys, namely SDSS, 2dFLenS, and 2dFGRS. We find stable behavior and consistent improvement over ANNz2 throughout. We finally apply Hybrid-z trained on GAMA to the entire KiDS-Bright DR4 sample of 1.2 million galaxies. For these final predictions, we design a method of smoothing the input redshift distribution of the training set, to avoid propagation of features present in GAMA, related to its small sky area and large-scale structure imprint in its fields. Our work paves the way towards the best-possible photo-$z$s achievable with machine learning for any galaxy type both for the final KiDS-Bright DR5 data and for future deeper imaging, such as from the Legacy Survey of Space and Time.

14 pages, 7 figures

References in corpus (24)

• Array Programming with NumPy
• The fourth data release of the Kilo-Degree Survey: ugri imaging and nine-band optical-IR photometry over 1000 square degrees
• Galaxy And Mass Assembly (GAMA): Data Release 4 and the z < 0.1 total and z < 0.08 morphological galaxy stellar mass functions
• Photometric redshift estimation via deep learning
• A Galaxy Photometric Redshift Catalog for the Sloan Digital Sky Survey Data Release 6
• WISE x SuperCOSMOS photometric redshift catalog: 20 million galaxies over 3pi steradians
• Galaxy And Mass Assembly (GAMA): Assimilation of KiDS into the GAMA database
• Galaxy clustering in the DESI Legacy Survey and its imprint on the CMB
• The 2-degree Field Lensing Survey: design and clustering measurements
• Galaxy And Mass Assembly (GAMA): The galaxy luminosity function within the cosmic web
• Photometric Redshifts with the LSST: Evaluating Survey Observing Strategies
• Photometric Redshift Estimation with a Convolutional Neural Network: NetZ
• Bright galaxy sample in the Kilo-Degree Survey Data Release 4: selection, photometric redshifts, and physical properties
• KiDS-1000: Combined halo-model cosmology constraints from galaxy abundance, galaxy clustering and galaxy-galaxy lensing
• Investigating Deep Learning Methods for Obtaining Photometric Redshift Estimations from Images
• The fifth data release of the Kilo Degree Survey: Multi-epoch optical/NIR imaging covering wide and legacy-calibration fields
• Photometric redshift-aided classification using ensemble learning
• Photometric Redshifts from SDSS Images with an Interpretable Deep Capsule Network
• Galaxy morphoto-Z with neural Networks (GaZNets). I. Optimized accuracy and outlier fraction from Imaging and Photometry
• Clustering of red-sequence galaxies in the fourth data release ofthe Kilo-Degree Survey
• Mixture Models for Photometric Redshifts
• TEGLIE: Transformer encoders as strong gravitational lens finders in KiDS
• PICZL: Image-based Photometric Redshifts for AGN
• Enhancing Photometric Redshift Catalogs Through Color-Space Analysis: Application to KiDS-Bright Galaxies