Optimizing neural network techniques in classifying Fermi-LAT gamma-ray sources

Abstract

Machine learning is an automatic technique that is revolutionizing scientific research, with innovative applications and wide use in astrophysics. The aim of this study was to developed an optimized version of an Artificial Neural Network machine learning method for classifying blazar candidates of uncertain type detected by the Fermi Large Area Telescope (LAT) gamma-ray instrument. The initial study used information from gamma-ray light curves present in the LAT 4-year Source Catalog. In this study we used additionally gamma-ray spectra and multiwavelength data, and certain statistical methods in order to improve classification. The final result of this study increased the classification performance by about 80 per cent with respect to previous method, leaving only 15 unclassified blazars instead of 77 out of total 573 in the LAT catalog. Other blazars were classified into BL Lacs and FSRQ in ratio of about two to one, similar to previous study. In both studies a precision value of 90 per cent was used as a threshold for classification.

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Cited by in corpus (12)

• Classification of Blazar Candidates of Uncertain Type from the Fermi LAT 8-Year Source Catalog with an Artificial Neural Network
• Gradient boosting decision trees classification of blazars of uncertain type in the fourth Fermi-LAT catalog
• Machine learning methods for constructing probabilistic Fermi-LAT catalogs
• Artificial Neural Network Classification of 4FGL Sources
• Classification of blazar candidates of unknown type in Fermi 4LAC by unanimous voting from multiple Machine Learning Algorithms
• On the physical association of Fermi-LAT blazars with their low-energy counterparts
• Classification of the \emph{Fermi}-LAT Blazar Candidates of Uncertain type using eXtreme Gradient Boosting
• A search for dark matter among Fermi-LAT unidentified sources with systematic features in Machine Learning
• Deep Learning Blazar Classification based on Multi-frequency Spectral Energy Distribution Data
• Identifying the 3FHL Catalog. IV. Swift Observations of Unassociated Fermi-LAT 3FHL Sources
• Characterizing the Gamma-ray Emission Properties of the Globular Cluster M5 with the Fermi-LAT
• Exploring the nature of Galactic unassociated sources detected by the Fermi-LAT