Machine Learning Approach to Integral Field Unit Spectroscopy
  Observations: I. HII Region Kinematics
  SITELLE is a novel integral field unit spectroscopy instrument that has an
impressive spatial (11 by 11 arcmin), spectral coverage, and spectral
resolution (R=1-20000). SIGNALS is anticipated to obtain deep observations
(down to 3.6x10-17ergs s-1cm-2) of 40 galaxies, each needing complex and
substantial time to extract spectral information. We present a method that uses
Convolution Neural Networks (CNN) for estimating emission line parameters in
optical spectra obtained with SITELLE as part of the SIGNALS large program. Our
algorithm is trained and tested on synthetic data representing typical emission
spectra for HII regions based on Mexican Million Models database(3MdB) BOND
simulations. The network's activation map demonstrates its ability to extract
the dynamical (broadening and velocity) parameters from a set of 5 emission
lines (e.g. H{\alpha}, N[II] doublet, and S[II] doublet) in the SN3 (651-685
nm) filter of SITELLE. Once trained, the algorithm was tested on real SITELLE
observations in the SIGNALS program of one of the South West fields of M33. The
CNN recovers the dynamical parameters with an accuracy better than 5 km s-1 in
regions with a signal-to-noise ratio greater than 15 over the H{\alpha}line.
More importantly, our CNN method reduces calculation time by over an order of
magnitude on the spectral cube with native spatial resolution when compared
with standard fitting procedures. These results clearly illustrate the power of
machine learning algorithms for the use in future IFU-based missions.
Subsequent work will explore the applicability of the methodology to other
spectral parameters such as the flux of key emission lines.
