2017
DOI: 10.1093/mnras/stx1413
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Using an artificial neural network to classify multicomponent emission lines with integral field spectroscopy from SAMI and S7

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 da… Show more

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Cited by 28 publications
(23 citation statements)
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“…We determined the number of components appropriate for each spaxel using an artificial neural net (LZComp; Hampton et al 2017), trained by five SAMI astronomers. We adopted the results from LZComp with the additional stipulation that the Hα emission line must have a signal-to-noise ratio of at least 5 in each component; if it did not, the number of components for that spaxel was reduced until the stipulation was met (or until only one component remained).…”
Section: Emission Line Fitsmentioning
confidence: 99%
“…We determined the number of components appropriate for each spaxel using an artificial neural net (LZComp; Hampton et al 2017), trained by five SAMI astronomers. We adopted the results from LZComp with the additional stipulation that the Hα emission line must have a signal-to-noise ratio of at least 5 in each component; if it did not, the number of components for that spaxel was reduced until the stipulation was met (or until only one component remained).…”
Section: Emission Line Fitsmentioning
confidence: 99%
“…Each spaxel is fit 3 times using lzifu to obtain one, two and three component fits to each emission line. The number of components in the multicomponent fits are determined using an artificial neural network trained by astronomers (for full details on the neural network, and precision success with SAMI data, see Hampton et al 2017).…”
Section: Emission-line Fittingmentioning
confidence: 99%
“…It would also be interesting to explore the extent to which the recent generation of integral field spectroscopic surveys may be able to bridge the two, applying techniques so far used only for star forming galaxies but instead looking at passive systems with some residual star formation. With spatially resolved spectra, any AGN contributions may be identified and excluded to isolate star formation signatures in otherwise passive galaxies (e.g., Hampton et al 2017;Medling et al 2018), allowing the Kennicutt (1983) and related approaches to be applied.…”
Section: Passive Galaxiesmentioning
confidence: 99%