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

• Published in: Monthly Notices of the Royal Astronomical Society
• Main Authors: Hampton, E. J., Medling, A. M.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Royal Astronomical Society 2017
• Subjects: sami
• Online Access: https://authors.library.caltech.edu/80837/; https://authors.library.caltech.edu/80837/1/stx1413.pdf; https://authors.library.caltech.edu/80837/2/1606.08133.pdf; https://resolver.caltech.edu/CaltechAUTHORS:20170828-095312567

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.