We give an overview of ISINA: INTEGRAL Source Identification Network Algorithm. This machine learning algorithm, using Random Forests, is applied to the IBIS/ISGRI dataset in order to ease the production of unbiased future soft gamma-ray source catalogues. The key steps of candidate searching, filtering and feature extraction are described. Three training and testing sets are created in order to deal with the diverse timescales and diverse objects encountered when dealing with the gamma-ray sky. Three independent Random Forest 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.
|Number of pages||8|
|Journal||AIP Conference Proceedings|
|State||Published - 2008|
|Event||Classification and Discovery in Large Astronomical Surveys - Ringberg Castle, Germany|
Duration: Oct 14 2008 → Oct 17 2008
- Machine learning