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Title: Broad Absorption Line Quasar catalogues with Supervised Neural Networks

Abstract

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.

Authors:
;  [1]; ;  [2]
  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)
Publication Date:
OSTI Identifier:
21254924
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Conference Proceedings; Journal Volume: 1082; Journal 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)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; ABSORPTION; ALGORITHMS; NEURAL NETWORKS; QUANTIZATION; QUASARS

Citation Formats

Scaringi, Simone, Knigge, Christian, Cottis, Christopher E., and Goad, Michael R. Broad Absorption Line Quasar catalogues with Supervised Neural Networks. United States: N. p., 2008. Web. doi:10.1063/1.3059042.
Scaringi, Simone, Knigge, Christian, Cottis, Christopher E., & Goad, Michael R. Broad Absorption Line Quasar catalogues with Supervised Neural Networks. United States. doi:10.1063/1.3059042.
Scaringi, Simone, Knigge, Christian, Cottis, Christopher E., and Goad, Michael R. Fri . "Broad Absorption Line Quasar catalogues with Supervised Neural Networks". United States. doi:10.1063/1.3059042.
@article{osti_21254924,
title = {Broad Absorption Line Quasar catalogues with Supervised Neural Networks},
author = {Scaringi, Simone and Knigge, Christian and Cottis, Christopher E. and Goad, Michael R.},
abstractNote = {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.},
doi = {10.1063/1.3059042},
journal = {AIP Conference Proceedings},
number = 1,
volume = 1082,
place = {United States},
year = {Fri Dec 05 00:00:00 EST 2008},
month = {Fri Dec 05 00:00:00 EST 2008}
}