Detecting Damped Lyman- Absorbers with Gaussian Processes
arXiv:1605.04460 · doi:10.1093/mnras/stx1958
Abstract
We develop an automated technique for detecting damped Lyman- absorbers (DLAs) along spectroscopic lines of sight to quasi-stellar objects (QSOs or quasars). The detection of DLAs in large-scale spectroscopic surveys such as SDSS-III sheds light on galaxy formation at high redshift, showing the nucleation of galaxies from diffuse gas. We use nearly 50 000 QSO spectra to learn a novel tailored Gaussian process model for quasar emission spectra, which we apply to the DLA detection problem via Bayesian model selection. We propose models for identifying an arbitrary number of DLAs along a given line of sight. We demonstrate our method's effectiveness using a large-scale validation experiment, with excellent performance. We also provide a catalog of our results applied to 162 858 spectra from SDSS-III data release 12.
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Cited by in corpus (22)
- Deep Learning of Quasar Spectra to Discover and Characterize Damped Lya Systems
- Statistical properties of damped Lyman-alpha systems from Sloan Digital Sky Survey DR12
- Constraining the H column density distribution at z3 from composite DLA spectra
- The First Large Absorption Survey in HI (FLASH): I. Science Goals and Survey Design
- Damped Lyman-alpha Absorbers from Sloan Digital Sky Survey DR16Q with Gaussian processes
- How proper are Bayesian models in the astronomical literature?
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- The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: The Damped Lyman- systems Catalog
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