Predicting galaxy spectra from images with hybrid convolutional neural networks

by   John F. Wu, et al.

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 deconvolution 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.



There are no comments yet.


page 2


QuasarNET: Human-level spectral classification and redshifting with Deep Neural Networks

We introduce QuasarNET, a deep convolutional neural network that perform...

Spectral classification using convolutional neural networks

There is a great need for accurate and autonomous spectral classificatio...

A Pilot Study For Fragment Identification Using 2D NMR and Deep Learning

This paper presents a method to identify substructures in NMR spectra of...

Cycle-StarNet: Bridging the gap between theory and data by leveraging large datasets

Spectroscopy provides an immense amount of information on stellar object...

Active deep learning method for the discovery of objects of interest in large spectroscopic surveys

Current archives of the LAMOST telescope contain millions of pipeline-pr...

AGNet: Weighing Black Holes with Machine Learning

Supermassive black holes (SMBHs) are ubiquitously found at the centers o...

Peptide-Spectra Matching from Weak Supervision

As in many other scientific domains, we face a fundamental problem when ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.