A Gaussian process model of quasar spectral energy distributions

Andrew Miller, Albert Wu, Jeffrey Regier, Jon McAuliffe, Dustin Lang, Prabhat, David Schlegel, Ryan Adams

Research output: Contribution to journalConference article

1 Scopus citations

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 "photoz" 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.

Original languageEnglish (US)
Pages (from-to)2494-2502
Number of pages9
JournalAdvances in Neural Information Processing Systems
Volume2015-January
StatePublished - Jan 1 2015
Externally publishedYes
Event29th Annual Conference on Neural Information Processing Systems, NIPS 2015 - Montreal, Canada
Duration: Dec 7 2015Dec 12 2015

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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    Miller, A., Wu, A., Regier, J., McAuliffe, J., Lang, D., Prabhat, Schlegel, D., & Adams, R. (2015). A Gaussian process model of quasar spectral energy distributions. Advances in Neural Information Processing Systems, 2015-January, 2494-2502.