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Poster
A Gaussian Process Model of Quasar Spectral Energy Distributions
Andrew Miller · Albert Wu · Jeffrey Regier · Jon McAuliffe · Dustin Lang · Mr. Prabhat · David Schlegel · Ryan Adams

Thu Dec 08:00 AM -- 12:00 PM PST @ 210 C #16 #None

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.

Author Information

Andrew Miller (Harvard)
Albert Wu (Harvard)
Jeff Regier (UC Berkeley Statistics)
Jon McAuliffe (UC Berkeley)
Dustin Lang
Mr. Prabhat (LBL/NERSC)
David Schlegel
Ryan Adams (Harvard)

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