Predicting galaxy spectra from images with hybrid convolutional neural networks [article]

John F. Wu, J. E. G. Peek
2020 arXiv   pre-print
Galaxies can be described by features of their optical spectra such as oxygen emission lines, or morphological features such as spiral arms. Although spectroscopy provides a rich description of the physical processes that govern galaxy evolution, spectroscopic data are observationally expensive to obtain. We are able to robustly predict and reconstruct galaxy spectra directly from broad-band imaging. We present a powerful new approach using a hybrid convolutional neural network with
more » ... n instead of batch normalization; this hybrid CNN outperforms other models in our tests. The learned mapping between galaxy imaging and spectra will be transformative for future wide-field surveys, such as with the Vera C. Rubin Observatory and Nancy Grace Roman Space Telescope, by multiplying the scientific returns for spectroscopically-limited galaxy samples.
arXiv:2009.12318v1 fatcat:i4ephedeirgahcirrectrta23q