Publications (1)0 Total impact
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ABSTRACT: A growing number of indicators are now being used with some confidence to
measure the metallicity(Z) of photoionisation regions in planetary nebulae,
galactic HII regions(GHIIRs), extra-galactic HII regions(EGHIIRs) and HII
galaxies(HIIGs). However, a universal indicator valid also at high
metallicities has yet to be found. Here, we report on a new artificial
intelligence-based approach to determine metallicity indicators that shows
promise for the provision of improved empirical fits. The method hinges on the
application of an evolutionary neural network to observational emission line
data. The network's DNA, encoded in its architecture, weights and neuron
transfer functions, is evolved using a genetic algorithm. Furthermore,
selection, operating on a set of 10 distinct neuron transfer functions, means
that the empirical relation encoded in the network solution architecture is in
functional rather than numerical form. Thus the network solutions provide an
equation for the metallicity in terms of line ratios without a priori
assumptions. Tapping into the mathematical power offered by this approach, we
applied the network to detailed observations of both nebula and auroral
emission lines in the optical for a sample of 96 HII-type regions and we were
able to obtain an empirical relation between Z and S23 with a dispersion of
only 0.16 dex. We show how the method can be used to identify new diagnostics
as well as the nonlinear relationship supposed to exist between the metallicity
Z, ionisation parameter U and effective (or equivalent) temperature T*.
10/2007;
