Classification of blazar candidates of uncertain type from the Fermi LAT 8-yr source catalogue with an artificial neural network

被引：18
作者：
Kovacevic, M. [1 ]
Chiaro, G. [2 ]
Cutini, S. [1 ]
Tosti, G. [3 ]
机构：
[1] Ist Nazl Fis Nucl, Sez Perugia, I-06123 Perugia, Italy
[2] INAF Ist Astrofis Spaziale & Fis Cosm, I-20133 Milan, Italy
[3] Univ Perugia, Dipartimento Fis & Geol, I-06123 Perugia, Italy
来源：
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY | 2020年 / 493卷 / 02期
关键词：
methods: statistical; galaxies: active; BL Lacertae objects: general; gamma-rays: galaxies;
D O I：
10.1093/mnras/staa394
中图分类号：
P1 [天文学];
学科分类号：
0704 ;
摘要：
The Fermi Large Area Telescope (LAT) has detected more than 5000 gamma-ray sources in its first 8 yr of operation. More than 3000 of them are blazars. About 60 per cent of the Fermi-LAT blazars are classified as BL Lacertae objects (BL Lacs) or Flat Spectrum Radio Quasars (FSRQs), while the rest remain of uncertain type. The goal of this study was to classify those blazars of uncertain type, using a supervised machine learning method based on an artificial neural network, by comparing their properties to those of known gamma-ray sources. Probabilities for each of 1329 uncertain blazars to be a BL Lac or FSRQ are obtained. Using 90 per cent precision metric, 801 can be classified as BL Lacs and 406 as FSRQs while 122 still remain unclassified. This approach is of interest because it gives a fast preliminary classification of uncertain blazars. We also explored how different selections of training and testing samples affect the classification and discuss the meaning of network outputs.
引用
收藏
页码：1926 / 1935
页数：10
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