A Machine Learning Approach to Integral Field Unit Spectroscopy Observations: III. Disentangling Multiple Components in Hii regions

TL;DR

This paper presents a convolutional neural network framework trained on synthetic spectra to accurately identify the number of line-of-sight components in galaxy spectra, outperforming Bayesian methods and aiding in the analysis of complex merging systems.

Contribution

Developed a CNN-based method for determining the number of spectral components, improving accuracy and efficiency over traditional Bayesian inference in galaxy spectral analysis.

Findings

CNN outperforms Bayesian inference in accuracy

Method effectively characterizes multiple components in galaxy spectra

Application to NGC2207/IC2163 reveals different component structures in galaxy regions

Abstract

In the first two papers of this series (Rhea et al. 2020; Rhea et al. 2021), we demonstrated the dynamism of machine learning applied to optical spectral analysis by using neural networks to extract kinematic parameters and emission-line ratios directly from the spectra observed by the SITELLE instrument located at the Canada-France-Hawai'i Telescope. In this third installment, we develop a framework using a convolutional neural network trained on synthetic spectra to determine the number of line-of-sight components present in the SN3 filter (656--683nm) spectral range of SITELLE. We compare this methodology to standard practice using Bayesian Inference. Our results demonstrate that a neural network approach returns more accurate results and uses less computational resources over a range of spectral resolutions. Furthermore, we apply the network to SITELLE observations of the merging galaxy system NGC2207/IC2163. We find that the closest interacting sector and the central regions of the galaxies are best characterized by two line-of-sight components while the outskirts and spiral arms are well-constrained by a single component. Determining the number of resolvable components is crucial in disentangling different galactic components in merging systems and properly extracting their respective kinematics.