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

Abstract : In the first two papers of this series (Rhea et al. 2020b; 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.
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Submitted on : Wednesday, January 19, 2022 - 1:46:24 PM
Last modification on : Tuesday, May 17, 2022 - 9:38:01 AM
Long-term archiving on: : Wednesday, April 20, 2022 - 6:40:27 PM

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Carter Rhea, Laurie Rousseau-Nepton, Simon Prunet, Julie Hlavacek-Larrondo, R. Pierre Martin, et al.. A Machine Learning Approach to Integral Field Unit Spectroscopy Observations: III. Disentangling Multiple Components in H ii regions. The Astrophysical Journal, American Astronomical Society, 2021, 923 (2), pp.169. ⟨10.3847/1538-4357/ac2c66⟩. ⟨hal-03534347⟩

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