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Application of Machine Learning to Optical Spectra — Kinematic Constraints

Presentation #208.01 in the session “Star Formation on Galactic Scales”.

Published onJan 11, 2021
Application of Machine Learning to Optical Spectra — Kinematic Constraints

Machine learning is rapidly becoming another tool in an astronomer’s statistical toolbox. Recent papers have demonstrated machine learning’s ability to recover degraded data, estimate important photometric parameters, and decode spectra. In this talk, I will be focusing on the use of convolutional neural networks in decoding optical emission-line spectra. The imaging Fourier Transform Spectrometer, SITELLE, at the Canada-France-Hawai’i telescope creates exquisite data cubes with spectral resolution reaching R~10000 and over 4 million spatial pixels. While standard fitting techniques to parse out the kinematic parameters, such as the velocity and broadening of the emission lines, exist, the number of spectra in each cube makes these techniques computationally expensive. Using a convolutional neural network, we have demonstrated a recoverability rate comparable to traditional methods; more importantly, the computation time has been reduced from ~11 days to ~4 hours. Subsequent papers focus on extracting additional information, such as critical line flux ratios, from the optical spectra.

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