3FGLzoo: classifying 3FGL unassociated Fermi-LAT γ-ray sources by artificial neural networks
3FGLzoo: classifying 3FGL unassociated Fermi-LAT γ-ray sources by artificial neural networks
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DOI：
10.1093/mnras/stx1328
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发表时间：
2017-05
影响因子：
4.8
通讯作者：
D. Salvetti;G. Chiaro;G. L. Mura;D. Thompson
中科院分区：
文献类型：
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作者：
D. Salvetti;G. Chiaro;G. L. Mura;D. Thompson
在运行的头四年中，费米大面望远镜 (LAT) 检测到了 3033 个 $\gamma$ 射线发射源。在费米-LAT 第三源目录 (3FGL) 中，大约 50% 的源与可能的 $\gamma$ 射线发射体没有明确的关联。我们使用旨在区分 BL Lacs 和 FSRQ 的人工神经网络算法来研究 559 个 3FGL 非关联源的源子类，其特征是与活动星系核非常相似的 $\gamma$ 射线特性。根据我们的方法，我们可以将 271 个对象分类为 BL Lac 候选对象，将 185 个对象分类为 FSRQ 候选对象，仅剩下 103 个对象没有明确的分类。我们建议为 $\gamma$ 射线物体建立一个新的动物园，其中不确定类型源的百分比从 52% 下降到不到 10%。这项研究的结果为$\gamma$射线天空的总体提出了新的考虑，并将有助于规划重要样本以进行严格的分析和多波长观测活动。
In its first four years of operation, the Fermi Large Area Telescope (LAT) detected 3033 $\gamma$-ray emitting sources. In the Fermi-LAT Third Source Catalogue (3FGL) about 50% of the sources have no clear association with a likely $\gamma$-ray emitter. We use an artificial neural network algorithm aimed at distinguishing BL Lacs from FSRQs to investigate the source subclass of 559 3FGL unassociated sources characterised by $\gamma$-ray properties very similar to those of Active Galactic Nuclei. Based on our method, we can classify 271 objects as BL Lac candidates, 185 as FSRQ candidates, leaving only 103 without a clear classification. we suggest a new zoo for $\gamma$-ray objects, where the percentage of sources of uncertain type drops from 52% to less than 10%. The result of this study opens up new considerations on the population of the $\gamma$-ray sky, and it will facilitate the planning of significant samples for rigorous analyses and multiwavelength observational campaigns.