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

Aims: We present a custom support vector machine classification package forphotometric redshift estimation, including comparisons with other methods. Wealso explore the efficacy of including galaxy shape information in redshiftestimation. Support vector machines, a type of machine learning, utilizeoptimization theory and supervised learning algorithms to construct predictivemodels based on the information content of data in a way that can treatdifferent input features symmetrically.  Methods: The custom support vector machine package we have developed isdesignated SPIDERz and made available to the community. As test data forevaluating performance and comparison with other methods, we apply SPIDERz tofour distinct data sets: 1) the publicly available portion of the PHAT-1catalog based on the GOODS-N field with spectroscopic redshifts in the range $z< 3.6$, 2) 14365 galaxies from the COSMOS bright survey with photometric bandmagnitudes, morphology, and spectroscopic redshifts inside $z < 1.4$, 3) 3048galaxies from the overlap of COSMOS photometry and morphology with 3D-HSTspectroscopy extending to $z < 3.9$, and 4) 2612 galaxies with five-bandphotometric magnitudes and morphology from the All-wavelength Extended GrothStrip International Survey and $z < 1.57$.  Results: We find that SPIDER-z achieves results competitive with otherempirical packages on the PHAT-1 data, and performs quite well in estimatingredshifts with the COSMOS and AEGIS data, including in the cases of a largeredshift range ($0 < z < 3.9$). We also determine from analyses with both theCOSMOS and AEGIS data that the inclusion of morphological information does nothave a statistically significant benefit for photometric redshift estimationwith the techniques employed here.

Analysis of a Custom Support Vector Machine for Photometric Redshift Estimation and the Inclusion of Galaxy Shape Information