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arXiv · 1606.08133

Using an Artificial Neural Network to Classify Multi-component Emission Line Fits

Elise J Hampton·Brent Groves·Anne Medling·Rebecca Davies·Mike Dopita·I-Ting Ho·Melanie Kaasinen·Lisa Kewley·Sarah Leslie·Rob Sharp·Sarah M Sweet·Adam D Thomas·SAMI Survey Team·S7 Team
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Abstract

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.

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Elise J Hampton, Brent Groves, Anne Medling, Rebecca Davies, Mike Dopita, I-Ting Ho, Melanie Kaasinen, Lisa Kewley, Sarah Leslie, Rob Sharp, Sarah M Sweet, Adam D Thomas, SAMI Survey Team, S7 Team. 2016-06-27. Using an Artificial Neural Network to Classify Multi-component Emission Line Fits. https://doi.org/10.1093/mnras/stx1413

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