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

We conduct a comprehensive study of the effects of incorporating galaxymorphology information in photometric redshift estimation. Using machinelearning methods, we assess the changes in the scatter and catastrophic outlierfraction of photometric redshifts when galaxy size, ellipticity, S\'{e}rsicindex and surface brightness are included in training on galaxy samples fromthe SDSS and the CFHT Stripe-82 Survey (CS82). We show that by adding galaxymorphological parameters to full $ugriz$ photometry, only mild improvements areobtained, while the gains are substantial in cases where fewer passbands areavailable. For instance, the combination of $grz$ photometry and morphologicalparameters almost fully recovers the metrics of $5$-band photometric redshifts.We demonstrate that with morphology it is possible to determine useful redshiftdistribution $N(z)$ of galaxy samples without any colour information. We alsofind that the inclusion of quasar redshifts and associated object sizes intraining improves the quality of photometric redshift catalogues, compensatingfor the lack of a good star-galaxy separator. We further show thatmorphological information can mitigate biases and scatter due to badphotometry. As an application, we derive both point estimates and posteriordistributions of redshifts for the official CS82 catalogue, training onmorphology and SDSS Stripe-82 $ugriz$ bands when available. Our redshifts yielda 68th percentile error of $0.058(1+z)$, and a catastrophic outlier fraction of$5.2$ per cent. We further include a deep extension trained on morphology andsingle $i$-band CS82 photometry.

Morpho-z: improving photometric redshifts with galaxy morphology