Paper:	Machine Learning SITELLE Spectra Kinematics Analysis
Volume:	532, ASTRONOMICAL DATA ANALYSIS SOFTWARE AND SYSTEMS XXX
Page:	501
Authors:	Rhea, C.; Rousseau-Nepton, L.; Prunet, S.; Hlavacek-Larrondo, J.; Fabbro, S.
Abstract:	We describe the first paper in a series of works in which we explore the application of different machine learning algorithms to the spectral analysis of extra galactic emission regions. We discuss the creation of synthetic spectra replicating the primary optical filter of the SITELLE instrument located at the Canada-France-Hawaii Telescope. We employ a convolutional neural network to learn the kinematic parameters, velocity and line broadening, directly from these spectra. Subsequently, we apply our methodology to a field of the nearby galaxy M33 and demonstrate the efficacy of our results in terms of residuals and computational expediency. We develop an open source framework for users to port this methodology to other IFUs, and we discuss future applications of machine learning to spectral analysis.

Astronomical Society of the Pacific
Conference Series

© Copyright 1988 - 2024 - Astronomical Society of the Pacific
No part of the material protected by this copyright may be reproduced or used in any form other than for personal use without written permission from the ASP.

Page generated in 0.094 seconds