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<ri:Resource created="2025-02-19T08:15:33Z" status="active" updated="2025-05-21T20:01:58Z" 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>Attributes of DESI DR9 spiral galaxies</title><shortName>J/A+A/694/A271</shortName><identifier>ivo://CDS.VizieR/J/A+A/694/A271</identifier><altIdentifier>doi:10.26093/cds/vizier.36940271</altIdentifier><curation><publisher ivo-id="ivo://CDS">CDS</publisher><creator><name>Cai J.-H.</name></creator><creator><name>Li N.</name></creator><creator><name>Yang H.-F.</name></creator><creator><name>Wang L.-L.</name></creator><creator><name>Zheng A.-Y.</name></creator><creator><name>Hao J.</name></creator><creator><name>Zhao X.,Xun Y.</name></creator><date role="Updated">2025-05-21T20:01:58Z</date><date role="Created">2025-02-19T08:15:33Z</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>photometry</subject><subject>chemical-abundances</subject><description>The gas-phase metallicity is a crucial parameter for understanding the evolution of galaxies. Considering that the number of multi-band galaxy images can typically reach tens of millions, using these images as input data for predicting gas-phase metallicity has become a feasible method. However, the accuracy of metallicity estimates from images is relatively limited. To solve this problem, we propose Galaxy Parameter Measurement Residual Network (GPM-ResNet), a deep learning method designed to predict gas-phase metallicity from photometric images of DESI. The parameter of photometric images are labeled with gas-phase metallicity values, which are obtained through spectroscopic methods with high accuracy. These labeled images serve as the training dataset for the GPM-ResNet method. GPM-ResNet mainly consists of two modules: a multi-order feature extractor and a parameter generator, enhancing the ability to effectively extract features related to gas-phase metallicity from photometric images. The {sigma} of Zpred-Ztrue is 0.12dex, which significantly outperforms the predicted results of the second-order polynomial ({sigma}=0.16dex) and the third-order polynomial ({sigma}=0.16dex) fitted using the color-metallicity relation on the same dataset. To further emphasize the superiority of GPM-ResNet, we analyze the predicted results on various network architectures, galaxy sizes, image resolutions, and wavelength bands of images. Moreover, we explore the Mass-Metallicity relation and recover the relation successfully by utilizing the predicted values Zpred. Finally, we apply GPM-ResNet to predict the gas-phase metallicity of spiral (EXP) galaxies observed by DESI, resulting in a comprehensive catalog containing 5095815 data.</description><source format="bibcode">2025A&amp;A...694A.271C</source><referenceURL>https://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/694/A271</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/VII/292">VII/292 : DESI Legacy Imaging Surveys DR8 photometric redshifts (Duncan, 2022)</relatedResource><relatedResource>https://www.legacysurvey.org/dr9/description : DESI DR9 Home 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