On the deduction of galaxy abundances with evolutionary neural networks

TL;DR

This paper introduces an evolutionary neural network approach to determine metallicity indicators in ionized regions, providing a flexible, empirical relation that improves metallicity estimation across different environments.

Contribution

The study presents a novel AI-based method using evolutionary neural networks to derive metallicity indicators without prior assumptions, enhancing empirical fits in astrophysical diagnostics.

Findings

Achieved a dispersion of 0.16 dex in metallicity estimates.

Applied the method to 96 HII regions with successful results.

Demonstrated the potential to identify new diagnostics and nonlinear relations.

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*.