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<ri:Resource created="2025-06-20T12:14:39Z" status="active" updated="2026-08-03T06:54:59Z" version="1.2" xmlns:cs="http://www.ivoa.net/xml/ConeSearch/v1.0" xmlns:ri="http://www.ivoa.net/xml/RegistryInterface/v1.0" xmlns:vr="http://www.ivoa.net/xml/VOResource/v1.0" xmlns:vs="http://www.ivoa.net/xml/VODataService/v1.1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.ivoa.net/xml/ConeSearch/v1.0 http://vo.ari.uni-heidelberg.de/docs/schemata/ConeSearch.xsd http://www.ivoa.net/xml/VOResource/v1.0 http://vo.ari.uni-heidelberg.de/docs/schemata/VOResource.xsd http://www.ivoa.net/xml/VODataService/v1.1 http://vo.ari.uni-heidelberg.de/docs/schemata/VODataService.xsd" xsi:type="vs:CatalogService"><title>KiDS-DR4 bright galaxy sample DL photo-z</title><shortName>J/A+A/698/A276</shortName><identifier>ivo://CDS.VizieR/J/A+A/698/A276</identifier><altIdentifier>doi:10.26093/cds/vizier/bc-nt/lchg</altIdentifier><curation><publisher ivo-id="ivo://CDS">CDS</publisher><creator><name>John William A.</name></creator><creator><name>Jalan P.</name></creator><creator><name>Bilicki M.</name></creator><creator><name>Hellwing W.A.</name></creator><creator><name>Thuruthipilly H.,Nakoneczny S.J.</name></creator><date role="Updated">2026-08-03T06:54:59Z</date><date role="Created">2025-06-20T12:14:39Z</date><contact><name>CDS support team</name><address>CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France</address><email>cds-question@unistra.fr</email></contact></curation><content><subject>galaxies</subject><subject>catalogs</subject><subject>redshifted</subject><subject>visible-astronomy</subject><description>We employ deep learning (DL) to improve photometric redshifts (photo-zs) in the Kilo-Degree Survey Data Release 4 Bright galaxy sample (KiDS-Bright DR4). This dataset, used as foreground for KiDS lensing and clustering studies, is flux-limited to r&lt;20mag with mean z=0.23 and covers 1000deg^2^. 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 over these previous redshift estimations by building a DL model, Hybrid-z, which 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 the level of 0.014(1+z) - representing a reduction by 20% over the previous nine-band derivations with ANNz2. Our photo-zs are robust and stable independently of galaxy magnitude, redshift, and color. 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.</description><source format="bibcode">2025A&amp;A...698A.276J</source><referenceURL>https://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/698/A276</referenceURL><type>Catalog</type><contentLevel>Research</contentLevel><relationship><relationshipType>IsServedBy</relationshipType><relatedResource ivo-id="ivo://CDS.VizieR/TAP">TAP VizieR generic service</relatedResource></relationship><relationship><relationshipType>IsServedBy</relationshipType><relatedResource>Conesearch service</relatedResource></relationship><relationship><relationshipType>related-to</relationshipType><relatedResource ivo-id="ivo://CDS.VizieR/II/383">II/383 : The ESO KiDS-DR5 multi-band source catalog (Wright+ 2024)</relatedResource></relationship></content><rights>https://cds.unistra.fr/vizier-org/licences_vizier.html</rights><capability><interface xsi:type="vr:WebBrowser"><accessURL 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