期刊
ASTROPHYSICAL JOURNAL
卷 923, 期 2, 页码 -出版社
IOP Publishing Ltd
DOI: 10.3847/1538-4357/ac2c66
关键词
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资金
- physics department of the Universite de Montreal
- IVADO
- FRQNT
- NSERC
- Canada Research Chair program
- Royal Society
- Newton Fund via Royal SocietyNewton Advanced Fellowship [NAF\R1\180403]
- Fundacao de Amparo a Pesquisa e Inovacao de Santa Catarina (FAPESC)
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)
In this series, machine learning is demonstrated to extract kinematic parameters and emission-line ratios from spectra with neural networks, and a new framework using a convolutional neural network for determining the number of line-of-sight components is developed. Results show that the neural network approach is more accurate and efficient compared to traditional methods, particularly in analyzing merging galaxy systems like NGC 2207/IC 2163.
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.
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