Finding SDSS BALQSOs Using Non-Negative Matrix Factorisation
James T. Allen, Paul C. Hewett, Vasily Belokurov, Vivienne Wild

TL;DR
This paper demonstrates that non-negative matrix factorisation (NMF) effectively decomposes SDSS quasar spectra, enabling the creation of a large, accurate BAL quasar catalogue with quantifiable error estimates, surpassing traditional PCA methods.
Contribution
The study introduces NMF as a fast, physically meaningful spectral decomposition technique for SDSS data, improving BAL quasar identification and error estimation over PCA.
Findings
NMF provides accurate spectral reconstructions.
The resulting BAL quasar catalogue is the largest available.
Quantitative error estimates for Balnicity Indices are derived.
Abstract
Modern spectroscopic databases provide a wealth of information about the physical processes and environments associated with astrophysical populations. Techniques such as blind source separation (BSS), in which sets of spectra are decomposed into a number of components, offer the prospect of identifying the signatures of the underlying physical emission processes. Principle Component Analysis (PCA) has been applied with some success but is severely limited by the inherent orthogonality restriction that the components must satisfy. Non-negative matrix factorisation (NMF) is a relatively new BSS technique that incorporates a non-negativity constraint on its components. In this respect, the resulting components may more closely reflect the physical emission signatures than is the case using PCA. We discuss some of the considerations that must be made when applying NMF and, through its…
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