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 present a new non-parametric method to quantify morphologies of galaxiesbased on a particular family of learning machines called support vectormachines. The method, that can be seen as a generalization of the classical CASclassification but with an unlimited number of dimensions and non-linearboundaries between decision regions, is fully automated and thus particularlywell adapted to large cosmological surveys. The source code is available fordownload at http://www.lesia.obspm.fr/~huertas/galsvm.html To test the method,we use a seeing limited near-infrared ($K_s$ band, $2,16\mu m$) sample observedwith WIRCam at CFHT at a median redshift of $z\sim0.8$. The machine is trainedwith a simulated sample built from a local visually classified sample from theSDSS chosen in the high-redshift sample's rest-frame (i band, $0.77\mu m$) andartificially redshifted to match the observing conditions. We use a12-dimensional volume, including 5 morphological parameters and othercaracteristics of galaxies such as luminosity and redshift. We show that aqualitative separation in two main morphological types (late type and earlytype) can be obtained with an error lower than 20% up to the completeness limitof the sample ($KAB\sim 22$) which is more than 2 times better that what wouldbe obtained with a classical C/A classification on the same sample and indeedcomparable to space data. The method is optimized to solve a specific problem,offering an objective and automated estimate of errors that enables astraightforward comparison with other surveys.

A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. I Method description