ISINA: INTEGRAL Source Identification Network Algorithm

TL;DR

ISINA is a machine learning-based algorithm utilizing Random Forests to identify gamma-ray sources in IBIS/ISGRI data, aiming to improve the creation of unbiased source catalogues.

Contribution

The paper introduces a novel application of Random Forests for gamma-ray source identification, including a new transient detection technique called the Transient Matrix.

Findings

High accuracy in source classification

Effective transient detection with the Transient Matrix

Robust performance across diverse gamma-ray source types

Abstract

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. First we introduce the dataset 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 timescales 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.