Broad absorption line quasar catalogues with supervised neural networks

Simone Scaringi, Christopher E. Cottis, Christian Knigge, Michael R. Goad

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


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.

Original languageEnglish
Pages (from-to)191-195
Number of pages5
JournalAIP Conference Proceedings
StatePublished - 2008
EventClassification and Discovery in Large Astronomical Surveys - Ringberg Castle, Germany
Duration: Oct 14 2008Oct 17 2008


  • Catalogues
  • Neural networks
  • Quasars: Broad absorption line
  • Surveys


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