2026-06-27T15:35:20Zhttps://docta.ucm.es/rest/oai/requestoai:docta.ucm.es:20.500.14352/49292023-08-27T22:47:34Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense
author
Gallego Maestro, Jesús
author
otros, ...
2023-06-16T14:23:52Z
2023-06-16T14:23:52Z
2021-11-19
0004-6361
10.1051/0004-6361/202141360
https://hdl.handle.net/20.500.14352/4929
http://dx.doi.org/10.1051/0004-6361/202141360
https://www.aanda.org/
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.
eng
Nonsequential neural network for simultaneous, consistent classification, and photometric redshifts of OTELO galaxies
journal article
URL
https://docta.ucm.es/bitstreams/53d11aef-442d-4f70-8b61-52b125445c86/download
File
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application/pdf
jesusgallego150libre.pdf
URL
https://docta.ucm.es/bitstreams/fd24825e-2bb8-4ff3-bbff-4017286fe60c/download
File
MD5
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text/plain
jesusgallego150libre.pdf.txt