Broad Absorption Line Quasar catalogues with Supervised Neural Networks

Journal Article · Fri Dec 05 00:00:00 EST 2008 · AIP Conference Proceedings

DOI:https://doi.org/10.1063/1.3059042· OSTI ID:21254924

Scaringi, Simone; Knigge, Christian ^[1]; Cottis, Christopher E; Goad, Michael R ^[2]

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1. Department of Physics and Astronomy, University of Southampton, Highfield, SO17 1BJ (United Kingdom)
2. Department of Physics and Astronomy, University of Leicester, University road, LE1 7RH (United Kingdom)

We have applied a Learning Vector Quantization (LVQ) algorithm to SDSS DR5 quasar spectra in order to create a large catalogue of broad absorption line quasars (BALQSOs). We first discuss the problems with BALQSO catalogues constructed using the conventional balnicity and/or absorption indices (BI and AI), and then describe the supervised LVQ network we have trained to recognise BALQSOs. The resulting BALQSO catalogue should be substantially more robust and complete than BI-or AI-based ones.

• OSTI ID: 21254924
• Journal Information: AIP Conference Proceedings, Vol. 1082, Issue 1; Conference: International conference on classification and discovery in large astronomical surveys, Ringberg Castle (Germany), 14-17 Oct 2008; Other Information: DOI: 10.1063/1.3059042; (c) 2008 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA); ISSN 0094-243X
• Country of Publication: United States
• Language: English

Related Subjects

71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS
ABSORPTION
ALGORITHMS
NEURAL NETWORKS
QUANTIZATION
QUASARS