We present The Machine, an artificial neural network (ANN) capable of differentiating between the numbers of Gaussian components needed to describe the emission lines of Integral Field Spectroscopic (IFS) observations. Here we show the preliminary results of the S7 first data release (Siding Spring Southern Seyfert Spectro- scopic Snapshot Survey, Dopita et al. 2015) and SAMI Galaxy Survey (Sydney-AAO Multi-object Integral Field Unit, Croom et al. 2012) to classify whether the emission lines in each spatial pixel are composed of 1, 2, or 3 different Gaussian components. Previously this classification has been done by individual people, taking an hour per galaxy. This time investment is no longer feasible with the large spectroscopic surveys coming online.
@article{arxiv.1606.08133,
title = {Using an Artificial Neural Network to Classify Multi-component Emission Line Fits},
author = {Elise J Hampton and Brent Groves and Anne Medling and Rebecca Davies and Mike Dopita and I-Ting Ho and Melanie Kaasinen and Lisa Kewley and Sarah Leslie and Rob Sharp and Sarah M Sweet and Adam D Thomas and SAMI Survey Team and S7 Team},
journal= {arXiv preprint arXiv:1606.08133},
year = {2018}
}