Nonsequential neural network for simultaneous, consistent classification, and photometric redshifts of OTELO galaxies

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Context. Computational techniques are essential for mining large databases produced in modern surveys with value-added products. Aims. This paper presents a machine learning procedure to carry out a galaxy morphological classification and photometric redshift estimates simultaneously. Currently, only a spectral energy distribution (SED) fitting has been used to obtain these results all at once. Methods. We used the ancillary data gathered in the OTELO catalog and designed a nonsequential neural network that accepts optical and near-infrared photometry as input. The network transfers the results of the morphological classification task to the redshift fitting process to ensure consistency between both procedures. Results. The results successfully recover the morphological classification and the redshifts of the test sample, reducing catastrophic redshift outliers produced by an SED fitting and avoiding possible discrepancies between independent classification and redshift estimates. Our technique may be adapted to include galaxy images to improve the classification.
© ESO 2021. Artículo firmado por 20 autores. The authors gratefully thank the anonymous referee for the constructive comments and recommendations, which helped improve the paper's readability and quality. This work was supported by the project Evolution of Galaxies, of reference AYA2014-58861-C3-1-P and AYA2017-88007-C3-1-P, within the "Programa estatal de fomento de la investigación científica y técnica de excelencia del Plan Estatal de Investigación Científica y Técnica y de Innovación (2013-2016" of the "Agencia Estatal de Investigación del Ministerio de Ciencia, Innovación y Universidades", and co-financed by the FEDER "Fondo Europeo de Desarrollo Regional". JAD is grateful for the support from the UNAM-DGAPA-PASPA 2019 program, the UNAM-CIC, the Canary Islands CIE: Tricontinental Atlantic Campus 2017, and the kind hospitality of the IAC. MP acknowledges financial supports from the Ethiopian Space Science and Technology Institute (ESSTI) under the Ethiopian Ministry of Innovation and Technology (MoIT), and from the Spanish Ministry of Economy and Competitiveness (MINECO) through projects AYA2013-42227-P and AYA2016-76682C3-1-P, and from the Spanish Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación through projects PID2019-106027GB-C41 and AYA2016-76682C3-1-P. APG, MSP and RPM were supported by the PNAYA project: AYA2017-88007-C3-2-P. JG was supported by the PNAYA project AYA2018-RTI-096188-B-i00. MC & APG are also funded by Spanish State Research Agency grant MDM-2017-0737 (Unidad de Excelencia María de Maeztu CAB). JIGS receives support through the Proyecto Puente 52.JU25.64661 (2018) funded by Sodercan S.A. and the Universidad de Cantabria, and PGC2018-099705-B-100 funded by the Ministerio de Ciencia, Innovación y Universidades. EJA acknowledges funding from the State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa" award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709) and from grant PGC2018-095049-B-C21. Based on observations made with the Gran Telescopio Canarias (GTC), installed in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias, in the island of La Palma. This work is (partly) based on data obtained with the instrument OSIRIS, built by a Consortium led by the Instituto de Astrofísica de Canarias in collaboration with the Instituto de Astronomía of the Universidad Autónoma de Mexico. OSIRIS was funded by GRANTECAN and the National Plan of Astronomy and Astrophysics of the Spanish Government.
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