Classifying spectra of emission-line regions with neural networks -- An application to integral field spectroscopic data of M33

TL;DR

This paper develops a neural network-based method to classify emission-line regions in galaxies using optical spectra, improving robustness over traditional diagnostics and handling line-of-sight superpositions, demonstrated on M33 data.

Contribution

The study introduces a neural network approach trained on simulated spectra to classify nebular regions in galaxies, outperforming traditional methods especially at low S/N and in complex line-of-sight scenarios.

Findings

Achieves ~80% accuracy at S/N(Hα)=20

Successfully classifies regions in M33, matching traditional diagnostics

Emulates traditional methods by focusing on strong lines at typical S/N levels

Abstract

Emission-line regions are key to understanding the properties of galaxies, as they trace the exchange of matter and energy between stars and the interstellar medium (ISM). In nearby galaxies, individual nebulae can be identified as HII regions, planetary nebulae (PNe), supernova remnants (SNR), and diffuse ionised gas (DIG) with criteria on single or multiple emission-line ratios. However, these methods are limited by rigid classification boundaries, the narrow scope of information they are based upon, and the inability to account for line-of-sight nebular superpositions. In this work, we use artificial neural networks to classify these regions using their optical spectra. Our training set consists of simulated spectra, obtained from photoionisation and shock models, and processed to match observations obtained with MUSE. We evaluate the performance of the network on simulated spectra for a range of signal-to-noise (S/N) levels and dust extinction, and the superposition of different nebulae along the line of sight. At infinite S/N the network achieves perfect predictive performance, while as the S/N decreases, the classification accuracy declines, reaching an average of ~80% at S/N(H$\alpha$)=20. We apply our model to real spectra from MUSE observations of the galaxy M33, where it provides a robust classification of individual spaxels, even at low S/N, identifying HII regions and PNe and distinguishing them from SNRs and diffuse ionized gas, while identifying overlapping nebulae. We then compare the network's classification with traditional diagnostics and find satisfactory agreement. Using activation maximisation maps, we find that at high S/N the model mainly relies on weak lines (e.g. auroral lines of metal ions and He recombination lines), while at the S/N level typical of our dataset the model effectively emulates traditional diagnostic methods by leveraging strong nebular lines.