A Machine-learning Approach to Integral Field Unit Spectroscopy Observations. I. H ii Region Kinematics

Abstract

Abstract SITELLE is a novel integral field unit spectroscopy instrument that has an impressive spatial (11 by 11 arcmin), spectral coverage, and spectral resolution (R ∼ 1–20,000). SIGNALS is anticipated to obtain deep observations (down to 3.6 × 10−17erg s−1 cm−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 H ii 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 five emission lines (e.g., Hα, 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 southwest fields of M33. The CNN recovers the dynamical parameters with an accuracy better than 5 in regions with a signal-to-noise ratio greater than 15 over the Hα 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.