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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A growing number of indicators are now being used with some confidence tomeasure the metallicity(Z) of photoionisation regions in planetary nebulae,galactic HII regions(GHIIRs), extra-galactic HII regions(EGHIIRs) and HIIgalaxies(HIIGs). However, a universal indicator valid also at highmetallicities has yet to be found. Here, we report on a new artificialintelligence-based approach to determine metallicity indicators that showspromise for the provision of improved empirical fits. The method hinges on theapplication of an evolutionary neural network to observational emission linedata. The network's DNA, encoded in its architecture, weights and neurontransfer functions, is evolved using a genetic algorithm. Furthermore,selection, operating on a set of 10 distinct neuron transfer functions, meansthat the empirical relation encoded in the network solution architecture is infunctional rather than numerical form. Thus the network solutions provide anequation for the metallicity in terms of line ratios without a prioriassumptions. Tapping into the mathematical power offered by this approach, weapplied the network to detailed observations of both nebula and auroralemission lines in the optical for a sample of 96 HII-type regions and we wereable to obtain an empirical relation between Z and S23 with a dispersion ofonly 0.16 dex. We show how the method can be used to identify new diagnosticsas well as the nonlinear relationship supposed to exist between the metallicityZ, ionisation parameter U and effective (or equivalent) temperature T*.

On the deduction of galaxy abundances with evolutionary neural networks