Hybrid-z: Enhancing the Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning
Creators
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1.
Center for Theoretical Physics
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2.
National Centre for Nuclear Research
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3.
California Institute of Technology
Abstract
We employed deep learning to improve the photometric redshifts (photo-zs) in the Kilo-Degree Survey Data Release 4 bright galaxy sample (KiDS-DR4 Bright). This dataset, used as foreground for KiDS lensing and clustering studies, is flux-limited to r < 20 mag with mean z = 0.23 and covers 1000 deg2. Its photo-zs were previously derived with artificial neural networks from the ANNz2 package trained on the Galaxy And Mass Assembly (GAMA) spectroscopy. Here, we considerably improve on these previous redshift estimations by building a deep learning model, Hybrid-z, that combines an inception-based convolutional neural network operating on four-band KiDS images with an artificial neural network using nine-band magnitudes from KiDS+VIKING. The Hybrid-z framework provides state-of-the-art photo-zs for KiDS-Bright with negligible mean residuals of O(10−4) and scatter at a level of 0.014(1 + z) – representing a reduction of 20% compared to the previous nine-band derivations with ANNz2. Our photo-zs are robust and stable independently of galaxy magnitude, redshift, and color. In fact, for blue galaxies, which typically have more pronounced morphological features, Hybrid-z provides a larger improvement over ANNz2 than for red galaxies. We checked 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 found stable behavior and consistent improvement over ANNz2 throughout. Finally, we applied Hybrid-z trained on GAMA to the entire KiDS-Bright DR4 sample of 1.2 million galaxies. For these final predictions, we designed a method of smoothing the input redshift distribution of the training set in order 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 toward the best-possible photo-zs achievable with machine learning for any galaxy type for both the final KiDS-Bright DR5 data and for future deeper imaging, such as from the Legacy Survey of Space and Time.
Copyright and License
© The Authors 2025.
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Acknowledgement
We would like to thank Elisa Chisari, Rui Li, Nicola Napolitano & Angus Wright for their valuable comments and suggestions on the manuscript. Based on data products from observations made with ESO Telescopes at the La Silla Paranal Observatory under program IDs 177.A-3016, 177.A-3017 and 177.A-3018, and on data products produced by Target/OmegaCEN, INAF-OACN, INAF-OAPD and the KiDS production team, on behalf of the KiDS consortium. OmegaCEN and the KiDS production team acknowledge support by NOVA and NWO-M grants. Members of INAF-OAPD and INAF-OACN also acknowledge the support from the Department of Physics & Astronomy of the University of Padova, and of the Department of Physics of Univ. Federico II (Naples). GAMA is a joint European-Australasian project based around a spectroscopic campaign using the Anglo-Australian Telescope. The GAMA input catalog is based on data taken from the Sloan Digital Sky Survey and the UKIRT Infrared Deep Sky Survey. Complementary imaging of the GAMA regions is being obtained by a number of independent survey programs including GALEX MIS, VST KiDS, VISTA VIKING, WISE, Herschel-ATLAS, GMRT, and ASKAP providing UV to radio coverage. GAMA is funded by the STFC (UK), the ARC (Australia), the AAO, and the participating institutions. The GAMA website is http://www.gama-survey.org/. This work is supported by the Polish National Science Center through grants no. 2020/38/E/ST9/00395, and 2018/31/G/ST9/03388. We have made use of TOPCAT (Taylor 2005) and STILTS (Taylor 2006) software, as well as of PYTHON (www.python.org), including the packages NUMPY (Harris et al. 2020), SCIPY (Virtanen et al. 2020), and MATPLOTLIB (Hunter 2007).
Data Availability
The photometric redshift catalog based on the Hybrid-z DL model, containing redshifts for over 1.2 million galaxies in the KiDS-Bright DR4 sample, is available at the CDS via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/698/A276
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Additional details
Related works
- Is new version of
- Discussion Paper: arXiv:2501.01942 (arXiv)
- Is supplemented by
- Dataset: https://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/698/A276 (URL)
Funding
- National Science Center
- 2020/38/E/ST9/00395
- National Science Center
- 2018/31/G/ST9/03388
Dates
- Accepted
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2025-05-05
- Available
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2025-06-20Published online