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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

A non-parametric smoothing method is presented that reduces noise inmulti-wavelength imaging data sets. Using Principle Component Analysis(hereafter PCA) to associate pixels according to their $ugriz$-band colors,smoothing is done over pixels with a similar location in PCA space. This methodsmoothes over pixels with similar color, which reduces the amount of mixing ofdifferent colors within the smoothing region. The method is tested using a mockgalaxy with signal-to-noise levels and color characteristics of SDSS data. Whencomparing this method to smoothing methods using a fixed radial profile or anadaptive radial profile, the chi^2-like statistic for the method presented hereis smaller. The method shows a small dependence on input parameters. Runningthis method on SDSS data and fitting theoretical stellar population models tothe smoothed data of the mock galaxy and SDSS data, shows that the methodreduces scatter in the best-fit stellar population analysis parameters, whencompared to cases where no smoothing is done. For an area centered on the starforming region of the mock galaxy, the median and standard deviation of thePCA-smoothed data is 7 Myr (+/- 3 Myr), as compared to 10 Myr (+/- 1 Myr) for asimple radial average, where the noise-free true value is 7.5 Myr (+/- 3.7Myr).

Preserving Structure in Multi-wavelength Images of Extended Objects