Broad absorption line quasar catalogues with supervised neural networks

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

doi:10.1063/1.3059042

Broad absorption line quasar catalogues with supervised neural networks

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

Physics

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

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.

Original language	English
Pages (from-to)	191-195
Number of pages	5
Journal	AIP Conference Proceedings
Volume	1082
DOIs	https://doi.org/10.1063/1.3059042
State	Published - 2008
Event	Classification and Discovery in Large Astronomical Surveys - Ringberg Castle, Germany Duration: Oct 14 2008 → Oct 17 2008

Keywords

Catalogues
Neural networks
Quasars: Broad absorption line
Surveys

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10.1063/1.3059042

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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.",

keywords = "Catalogues, Neural networks, Quasars: Broad absorption line, Surveys",

author = "Simone Scaringi and Cottis, {Christopher E.} and Christian Knigge and Goad, {Michael R.}",

year = "2008",

doi = "10.1063/1.3059042",

language = "English",

volume = "1082",

pages = "191--195",

journal = "AIP Conference Proceedings",

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note = "Classification and Discovery in Large Astronomical Surveys ; Conference date: 14-10-2008 Through 17-10-2008",

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TY - JOUR

T1 - Broad absorption line quasar catalogues with supervised neural networks

AU - Scaringi, Simone

AU - Cottis, Christopher E.

AU - Knigge, Christian

AU - Goad, Michael R.

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

KW - Catalogues

KW - Neural networks

KW - Quasars: Broad absorption line

KW - Surveys

UR - http://www.scopus.com/inward/record.url?scp=61849132798&partnerID=8YFLogxK

U2 - 10.1063/1.3059042

DO - 10.1063/1.3059042

M3 - Conference article

AN - SCOPUS:61849132798

SN - 0094-243X

VL - 1082

SP - 191

EP - 195

JO - AIP Conference Proceedings

JF - AIP Conference Proceedings

T2 - Classification and Discovery in Large Astronomical Surveys

Y2 - 14 October 2008 through 17 October 2008

ER -

Broad absorption line quasar catalogues with supervised neural networks

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Keywords

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