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ISINA：INTEGRAL源识别网络算法

ISINA: INTEGRAL Source Identification Network Algorithm

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

备注：
11 pages, 10 figures and 2 tables. Accepted for publication in MNRAS
发表信息：
2008MNRAS.390.1339S
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摘要：

介绍了ISINA：INTEGRAL源识别网络算法.这种机器学习算法，使用随机森林，应用于IBIS/ISGRI数据集，以方便生产无偏的未来软伽马射线源目录。首先，我们介绍了数据集和处理使用编码掩码技术获得的图像时遇到的问题。引入了源候选搜索的初始步骤，并创建了初始候选列表。然后，对初始候选列表的特征提取的描述与这些候选的特征合并一起执行。创建了三个训练和测试集，以处理在处理伽马射线天空时遇到的不同时间尺度。建立了三个独立的随机森林：一个处理弱持续源识别，一个处理强持续源，最后一个处理瞬态。对于后者，介绍和描述了一种新的瞬态检测技术：瞬态矩阵。最后，网络的性能进行评估和讨论使用测试集和一些说明性的源例子。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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