A Gaussian Process Model of Quasar Spectral Energy Distributions
Abstract
We propose a method for combining two sources of astronomical data, spectroscopy and photometry, that carry information about sources of light (e.g., stars, galaxies, and quasars) at extremely different spectral resolutions. Our model treats the spectral energy distribution (SED) of the radiation from a source as a latent variable that jointly explains both photometric and spectroscopic observations. We place a flexible, nonparametric prior over the SED of a light source that admits a physically interpretable decomposition, and allows us to tractably perform inference. We use our model to predict the distribution of the redshift of a quasar from five-band (low spectral resolution) photometric data, the so called ``photo-z'' problem. Our method shows that tools from machine learning and Bayesian statistics allow us to leverage multiple resolutions of information to make accurate predictions with well-characterized uncertainties.
Cite
Text
Miller et al. "A Gaussian Process Model of Quasar Spectral Energy Distributions." Neural Information Processing Systems, 2015.Markdown
[Miller et al. "A Gaussian Process Model of Quasar Spectral Energy Distributions." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/miller2015neurips-gaussian/)BibTeX
@inproceedings{miller2015neurips-gaussian,
title = {{A Gaussian Process Model of Quasar Spectral Energy Distributions}},
author = {Miller, Andrew and Wu, Albert and Regier, Jeff and McAuliffe, Jon and Lang, Dustin and Prabhat, Mr. and Schlegel, David and Adams, Ryan P.},
booktitle = {Neural Information Processing Systems},
year = {2015},
pages = {2494-2502},
url = {https://mlanthology.org/neurips/2015/miller2015neurips-gaussian/}
}