Finding Galaxies in the Shadows of Quasars with Gaussian Processes

Roman Garnett, Shirley Ho, Jeff Schneider
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1025-1033, 2015.

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.

Cite this Paper

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.} }
Endnote
%0 Conference Paper %T Finding Galaxies in the Shadows of Quasars with Gaussian Processes %A Roman Garnett %A Shirley Ho %A Jeff Schneider %B Proceedings of the 32nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2015 %E Francis Bach %E David Blei %F pmlr-v37-garnett15 %I PMLR %P 1025--1033 %U https://proceedings.mlr.press/v37/garnett15.html %V 37 %X 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.
RIS
TY - CPAPER TI - Finding Galaxies in the Shadows of Quasars with Gaussian Processes AU - Roman Garnett AU - Shirley Ho AU - Jeff Schneider BT - Proceedings of the 32nd International Conference on Machine Learning DA - 2015/06/01 ED - Francis Bach ED - David Blei ID - pmlr-v37-garnett15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 37 SP - 1025 EP - 1033 L1 - http://proceedings.mlr.press/v37/garnett15.pdf UR - https://proceedings.mlr.press/v37/garnett15.html AB - 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. ER -
APA
Garnett, R., Ho, S. & Schneider, J.. (2015). Finding Galaxies in the Shadows of Quasars with Gaussian Processes. Proceedings of the 32nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 37:1025-1033 Available from https://proceedings.mlr.press/v37/garnett15.html.

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