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Using an artificial neural network to classify multicomponent emission lines with integral field spectroscopy from SAMI and S7

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dc.contributor.author Hampton, E. J
dc.contributor.author Medling, A. M
dc.contributor.author Groves, B
dc.contributor.author Kewley, L
dc.contributor.author Dopita, M
dc.contributor.author Davies, R
dc.contributor.author Ho, I-Ting
dc.contributor.author Kaasinen, M
dc.contributor.author Leslie, S
dc.contributor.author Sharp, R
dc.contributor.author Sweet, S. M
dc.contributor.author Thomas, A.
dc.date.accessioned 2020-11-10T13:43:48Z
dc.date.available 2020-11-10T13:43:48Z
dc.date.issued 2017-09
dc.identifier.citation Monthly Notices of the Royal Astronomical Society, Vol. 470, No. 3, pp. 3395 - 3416 en_US
dc.identifier.issn 1365-2966
dc.identifier.uri http://prints.iiap.res.in/handle/2248/6795
dc.description Restricted Access © Royal Astronomical Society https://doi.org/10.1093/mnras/stx1413 en_US
dc.description.abstract Integral field spectroscopy (IFS) surveys are changing how we study galaxies and are creating vastly more spectroscopic data available than before. The large number of resulting spectra makes visual inspection of emission line fits an infeasible option. Here, we present a demonstration of an artificial neural network (ANN) that determines the number of Gaussian components needed to describe the complex emission line velocity structures observed in galaxies after being fit with LZIFU. We apply our ANN to IFS data for the S7 survey, conducted using the Wide Field Spectrograph on the ANU 2.3 m Telescope, and the SAMI Galaxy Survey, conducted using the SAMI instrument on the 4 m Anglo-Australian Telescope. We use the spectral fitting code LZIFU (Ho et al. 2016a) to fit the emission line spectra of individual spaxels from S7 and SAMI data cubes with 1-, 2- and 3-Gaussian components. We demonstrate that using an ANN is comparable to astronomers performing the same visual inspection task of determining the best number of Gaussian components to describe the physical processes in galaxies. The advantage of our ANN is that it is capable of processing the spectra for thousands of galaxies in minutes, as compared to the years this task would take individual astronomers to complete by visual inspection. en_US
dc.language.iso en en_US
dc.publisher Oxford University Press on behalf of the Royal Astronomical Society en_US
dc.subject Methods: data analysis en_US
dc.subject |Techniques: imaging spectroscopy en_US
dc.subject Techniques: spectroscopic en_US
dc.subject Galaxies: general en_US
dc.subject Galaxies: kinematics and dynamics en_US
dc.title Using an artificial neural network to classify multicomponent emission lines with integral field spectroscopy from SAMI and S7 en_US
dc.type Article en_US


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