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

Despite the high accuracy of photometric redshifts (zphot) derived usingMachine Learning (ML) methods, the quantification of errors through reliableand accurate Probability Density Functions (PDFs) is still an open problem.First, because it is difficult to accurately assess the contribution fromdifferent sources of errors, namely internal to the method itself and from thephotometric features defining the available parameter space. Second, becausethe problem of defining a robust statistical method, always able to quantifyand qualify the PDF estimation validity, is still an open issue. We present acomparison among PDFs obtained using three different methods on the same dataset: two ML techniques, METAPHOR (Machine-learning Estimation Tool for AccuratePHOtometric Redshifts) and ANNz2, plus the spectral energy distributiontemplate fitting method, BPZ. The photometric data were extracted from the KiDS(Kilo Degree Survey) ESO Data Release 3, while the spectroscopy was obtainedfrom the GAMA (Galaxy and Mass Assembly) Data Release 2. The statisticalevaluation of both individual and stacked PDFs was done through quantitativeand qualitative estimators, including a dummy PDF, useful to verify whetherdifferent statistical estimators can correctly assess PDF quality. We concludethat, in order to quantify the reliability and accuracy of any zphot PDFmethod, a combined set of statistical estimators is required.

Statistical analysis of probability density functions for photometric redshifts through the KiDS-ESO-DR3 galaxies