Journal Article

ISINA: <i>INTEGRAL</i> Source Identification Network Algorithm*

S. Scaringi, A. J. Bird, D. J. Clark, A. J. Dean, A. B. Hill, V. A. McBride and S. E. Shaw

in Monthly Notices of the Royal Astronomical Society

Published on behalf of The Royal Astronomical Society

Volume 390, issue 4, pages 1339-1348
Published in print November 2008 | ISSN: 0035-8711
Published online October 2008 | e-ISSN: 1365-2966 | DOI:
ISINA: INTEGRAL Source Identification Network Algorithm*

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We give an overview of ISINA: INTEGRAL Source Identification Network Algorithm. This machine learning algorithm, using random forests, is applied to the IBIS/ISGRI data set in order to ease the production of unbiased future soft gamma-ray source catalogues. First, we introduce the data set and the problems encountered when dealing with images obtained using the coded mask technique. The initial step of source candidate searching is introduced and an initial candidate list is created. A description of the feature extraction on the initial candidate list is then performed together with feature merging for these candidates. Three training and testing sets are created in order to deal with the diverse time-scales encountered when dealing with the gamma-ray sky. Three independent random forests are built: one dealing with faint persistent source recognition, one dealing with strong persistent sources and a final one dealing with transients. For the latter, a new transient detection technique is introduced and described: the transient matrix. Finally the performance of the network is assessed and discussed using the testing set and some illustrative source examples.

Keywords: methods: data analysis; catalogues; surveys

Journal Article.  7951 words.  Illustrated.

Subjects: Astronomy and Astrophysics

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