ICML 2015poster5 citations
Finding Galaxies in the Shadows of Quasars with Gaussian Processes
Roman Garnett, Shirley Ho, Jeff Schneider
Abstract
We develop an automated technique for detecting damped Lyman-αabsorbers (DLAs) along spectroscopic sightlines to quasi-stellar objects (QSOs or quasars). The detection of DLAs in large-scale spectroscopic surveys such as SDSS–III is critical to address outstanding cosmological questions, such as the nature of galaxy formation. We use nearly 50000 QSO spectra to learn a tailored Gaussian process model for quasar emission spectra, which we apply to the DLA detection problem via Bayesian model selection. We demonstrate our method’s effectiveness with a large-scale validation experiment on over 100000 spectra, with excellent performance.
BibTeX
@InProceedings{pmlr-v37-garnett15,
title = {Finding Galaxies in the Shadows of Quasars with Gaussian Processes},
author = {Garnett, Roman and Ho, Shirley and Schneider, Jeff},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning},
pages = {1025--1033},
year = {2015},
editor = {Bach, Francis and Blei, David},
volume = {37},
series = {Proceedings of Machine Learning Research},
address = {Lille, France},
month = {07--09 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v37/garnett15.pdf},
url = {https://proceedings.mlr.press/v37/garnett15.html},
abstract = {We develop an automated technique for detecting damped Lyman-αabsorbers (DLAs) along spectroscopic sightlines to quasi-stellar objects (QSOs or quasars). The detection of DLAs in large-scale spectroscopic surveys such as SDSS–III is critical to address outstanding cosmological questions, such as the nature of galaxy formation. We use nearly 50000 QSO spectra to learn a tailored Gaussian process model for quasar emission spectra, which we apply to the DLA detection problem via Bayesian model selection. We demonstrate our method’s effectiveness with a large-scale validation experiment on over 100000 spectra, with excellent performance.}
}