English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Context. Computational techniques are essential for mining large databasesproduced in modern surveys with value-added products. Aims. This paper presentsa machine learning procedure to carry out simultaneously galaxy morphologicalclassification and photometric redshift estimates. Currently, only spectralenergy distribution (SED) fitting has been used to obtain these results all atonce. Methods. We used the ancillary data gathered in the OTELO catalog anddesigned a non-sequential neural network that accepts optical and near-infraredphotometry as input. The network transfers the results of the morphologicalclassification task to the redshift fitting process to ensure consistencybetween both procedures. Results. The results successfully recover themorphological classification and the redshifts of the test sample, reducingcatastrophic redshift outliers produced by SED fitting and avoiding possiblediscrepancies between independent classification and redshift estimates. Ourtechnique may be adapted to include galaxy images to improve theclassification.

Non-Sequential Neural Network for Simultaneous, Consistent Classification and Photometric Redshifts of OTELO Galaxies