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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 spectralanalysis by using neural networks to extract kinematic parameters andemission-line ratios directly from the spectra observed by the SITELLEinstrument located at the Canada-France-Hawai'i Telescope. In this thirdinstallment, we develop a framework using a convolutional neural networktrained on synthetic spectra to determine the number of line-of-sightcomponents present in the SN3 filter (656--683nm) spectral range of SITELLE. Wecompare this methodology to standard practice using Bayesian Inference. Ourresults demonstrate that a neural network approach returns more accurateresults and uses less computational resources over a range of spectralresolutions. Furthermore, we apply the network to SITELLE observations of themerging galaxy system NGC2207/IC2163. We find that the closest interactingsector and the central regions of the galaxies are best characterized by twoline-of-sight components while the outskirts and spiral arms arewell-constrained by a single component. Determining the number of resolvablecomponents is crucial in disentangling different galactic components in mergingsystems and properly extracting their respective kinematics.

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