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<ri:Resource created="2026-01-28T09:04:56Z" status="active" updated="2026-03-02T06:25:50Z" 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>Ultra-diffuse galaxies from DES DR2</title><shortName>J/A+A/706/A35</shortName><identifier>ivo://CDS.VizieR/J/A+A/706/A35</identifier><curation><publisher ivo-id="ivo://CDS">CDS</publisher><creator><name>Su Y.</name></creator><creator><name>Yi Z.</name></creator><creator><name>Li S.</name></creator><creator><name>Liu M.</name></creator><creator><name>Kong X.</name></creator><creator><name>Bu Y.</name></creator><creator><name>Li D.</name></creator><date role="Updated">2026-03-02T06:25:50Z</date><date role="Created">2026-01-28T09:04:56Z</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>visible-astronomy</subject><description>Ultra-diffuse galaxies (UDGs) are a class of galaxies characterized by extremely low surface brightness and large effective radius. The significance of UDGs lies in their unique properties, such as their diverse dark matter content, which challenge existing theories of galaxy formation and evolution. However, their low surface brightness and diffuse stellar distributions make UDGs particularly difficult to detect. To address this challenge, this study introduces UDGnet-DES, a deep learning-based object detection model, combined with an iterative training strategy, to conduct a large-scale search for UDG candidates in the Dark Energy Survey (DES) DR2 imaging data. Using our model, we searched UDGs from more than 500000 DES images and further filtered the results based on surface brightness and visual inspection. As a result, we obtained a catalog of 2991 UDG samples, of which 39 have spectroscopic redshifts. Analysis of our UDG samples reveals that most objects exhibit nearly circular morphologies. Among them, blue UDGs tend to have higher surface brightness, while red UDGs show significant spatial clustering, in contrast to the more uniformly distributed blue UDGs.The method developed in this study can improve the efficiency of UDG searches and will be applied to the search for UDGs in the China Space Station Telescope (CSST) survey project.</description><source format="bibcode">2026A&amp;A...706A..35S</source><referenceURL>https://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/706/A35</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/371">II/371 : The Dark Energy Survey 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