Classification of Blazar Candidates of Uncertain Type from the Fermi LAT 8-Year Source Catalog with an Artificial Neural Network

Abstract

The Fermi Large Area Telescope (LAT) has detected more than 5000 gamma-ray sources in its first 8 years 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.

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References in corpus (20)

• Fermi Large Area Telescope First Source Catalog
• The Spectral Energy Distribution of Fermi bright blazars
• The Third Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope
• The Fourth Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope
• A Statistical Approach to Recognizing Source Classes for Unassociated Sources in the First Fermi-LAT Catalog
• Classification and Ranking of Fermi LAT Gamma-ray Sources from the 3FGL Catalog using Machine Learning Techniques
• Application of the Gaussian mixture model in pulsar astronomy -- pulsar classification and candidates ranking for {\it Fermi} 2FGL catalog
• 3FGL Demographics Outside the Galactic Plane using Supervised Machine Learning: Pulsar and Dark Matter Subhalo Interpretations
• Optical spectroscopic observations of gamma-ray blazar candidates II. The 2013 KPNO campaign in the Northern Hemisphere
• Blazar Flaring Patterns (B-FlaP): Classifying Blazar Candidates of Uncertain type in the third Fermi-LAT catalog by Artificial Neural Networks
• Gamma-Ray Active Galactic Nucleus Type through Machine-Learning Algorithms
• Search for gamma-ray-emitting active galactic nuclei in the Fermi-LAT unassociated sample using machine learning
• Research and characterisation of blazar candidates among the Fermi/LAT 3FGL catalogue using multivariate classifications
• 3FGLzoo. Classifying 3FGL Unassociated Fermi-LAT Gamma-ray Sources by Artificial Neural Networks
• Evaluating the optical classification of Fermi BCUs using machine learning
• Classification of New X-ray Counterparts for Fermi Unassociated Gamma Ray Sources Using the Swift X-Ray Telescope
• The Cherenkov Telescope Array: Science Goals and Current Status
• Optimizing neural network techniques in classifying Fermi-LAT gamma-ray sources
• Evaluating Optical Classification for {\em Fermi} Blazar Candidates with a Statistical method using Broadband Spectral Indices
• Jets, black holes and disks in blazars

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