Nebular emission from composite star-forming galaxies -- I. A novel modelling approach

TL;DR

This paper presents a new machine learning-based modeling approach to simulate nebular emission in star-forming galaxies, accounting for complex composite structures and revealing biases in traditional abundance estimates.

Contribution

It introduces a novel method combining multiple HII region contributions with neural network predictions, enabling efficient simulation of galaxy emission-line properties.

Findings

Biases in oxygen-abundance estimates from traditional methods

Importance of modeling composite galaxy structures for accurate interpretation

Generated 250,000 synthetic galaxies with diverse properties

Abstract

We introduce a novel approach to modelling the nebular emission from star-forming galaxies by combining the contributions from many HII regions incorporating loose trends in physical properties, random dust attenuation, a predefined Halpha luminosity function and a diffuse ionized-gas component. Using a machine-learning-based regression artificial neural network trained on a grid of models generated by the photoionization code Cloudy, we efficiently predict emission-line properties of individual HII regions over a wide range of physical conditions. We generate 250,000 synthetic star-forming galaxies composed of up to 3000 HII regions and explore how variations in parameters affect their integrated emission-line properties. Our results highlight systematic biases in oxygen-abundance estimates derived using traditional methods, emphasizing the importance of accounting for the composite nature of star-forming galaxies when interpreting integrated nebular emission. Future work will leverage this approach to explore in detail its impact on parameter estimates of star-forming galaxies.