2021ApJ...923..169R -
Astrophys. J., 923, 169-169 (2021/December-3)
A machine-learning approach to integral field unit spectroscopy observations. III. Disentangling multiple components in H II regions.
RHEA C.L., ROUSSEAU-NEPTON L., PRUNET S., HLAVACEK-LARRONDO J., MARTIN R.P., GRASHA K., ASARI N.V., BEGIN T., VIGNERON B. and PRASOW-EMOND M.
Abstract (from CDS):
In the first two papers of this series, 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 (656-683 nm) 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 fewer computational resources over a range of spectral resolutions. Furthermore, we apply the network to SITELLE observations of the merging galaxy system NGC 2207/IC 2163. 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.
Abstract Copyright:
© 2021. The American Astronomical Society. All rights reserved.
Journal keyword(s):
Galaxies - H II regions - Gaseous nebulae
Simbad objects:
2
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