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

• Published in: arXiv.org 2021-10
• Main Authors: Carter Lee Rhea, Rousseau-Nepton, Laurie, Prunet, Simon, Hlavacek-Larrondo, Julie, Martin, R Pierre, Grasha, Kathryn, Natalia Vale Asari, Bégin, Théophile, Vigneron, Benjamin, Prasow-Émond, Myriam
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Bayesian analysis; Emission analysis; Emission spectra; Galactic structure; Galaxies; Kinematics; Line of sight; Machine learning; Neural networks; Spectrum analysis; Statistical inference
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2110.00569

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.