J/A+A/701/A223ivo://CDS.VizieR/J/A+A/701/A223doi:10.26093/cds/vizier/bc-nx/lccdCDSVale A.Paulino-Afonso A.Humphrey A.Cunha P.A.C.Ribeiro B.,Cerqueira B.Carvajal R.Fonseca J.2026-08-03T06:57:11Z2025-09-16T08:18:06ZCDS support team

CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France

cds-question@unistra.frgalaxiesphotometrycatalogsastronomical-modelsThe identification of Lyman-alpha emitting galaxies (LAEs) has traditionally relied on dedicated surveys using custom narrow-band filters, which constrain observations to specific narrow redshift intervals, or on blind spectroscopy, which - although unbiased - typically requires extensive telescope time, making it challenging to assemble large, statistically robust galaxy samples. With the advent of wide-area astronomical surveys producing datasets significantly larger than traditional surveys, the need for new techniques arises. We test whether gradient boosting algorithms, trained on broadband photometric data from traditional LAE surveys, can efficiently and accurately identify LAE candidates from typical star-forming galaxies at similar redshifts and brightness levels. Using galaxy samples at z {in} [2, 6] derived from the COSMOS2020 and SC4K catalogs, we trained gradient-boosting machine learning algorithms (LGBM, XGBoost, and CatBoost), using optical and near-infrared broad-band photometry. To ensure balanced performance, the models were trained on carefully selected datasets, with similar redshift and i-band magnitude distributions. Additionally, the models were tested for robustness by perturbing the photometric data using the associated observational uncertainties. Our classification models achieved F1-scores ~87%, successfully identifying around 7000 objects with unanimous agreement across all models. This more than doubles the number of LAEs identified in the COSMOS field compared with the SC4K dataset. We managed to spectroscopically confirmed 60 of these LAEs candidates using the publicly available catalogs in the COSMOS field. These results highlight the potential of machine learning in efficiently identifying LAEs candidates, laying foundations for application to larger photometric surveys, such as Euclid and LSST. By complementing traditional approaches and providing robust pre-selection capabilities, our models facilitate the analysis of these objects, crucial to increase our knowledge of the overall LAE population.2025A&A...701A.223Vhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/701/A223CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toJ/MNRAS/476/4725 : SC4K catalogue of candidate LAEs (Sobral+, 2018)J/ApJS/258/11 : The COSMOS2020 catalog (Weaver+, 2022)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/701/A223https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/701/A223http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/701/A223https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/701/A223https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/701/A223http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/701/A223GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/A+A/701/A223/seldata (Identification of the selected candidates)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/A+A/701/A223/seldata?https://vizier.iucaa.in/viz-bin/conesearch/J/A+A/701/A223/seldata?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/A+A/701/A223/seldata?GETtext/xml+votable180.050000true150.75528010921291.6038728403070.0055555555555555565/6814 6/27245 27247 27344https://cdsarc.cds.unistra.fr/viz-bin/moc/J/A+A/701/A223?format=asciidefaultJ/A+A/701/A223/seldataIdentification of the selected candidatesrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintIDCOSMOS2020 ID numbermeta.id;meta.mainintRAJ2000COSMOS2020 Right Ascension (J2000.0)degpos.eq.ra;meta.mainDEJ2000COSMOS2020 Declination (J2000.0)degpos.eq.dec;meta.mainzphCOSMOS2020 LePhare redshift (lp_zBEST)src.redshiftfloatTimesPredNumber of predictions (out of 15)meta.numberintAvgPredProbaAverage prediction probability of LAE candidatestat.fit.goodnessdouble