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.
