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Table · dataset · 2020

4FGL blazar classification neural network

Listed in IVOA Registry (Virtual Observatory)

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

Description

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.

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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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Stated by source
Gamma-Ray
Provenance · 1 source records, 8 field assertions
SourceKeyLast seenRaw
IVOA Registry (Virtual Observatory)ivo://cds.vizier/j/mnras/493/192612 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · IVOA Registryconnector:ivoa_registry@1.0.0
concepts[field].local:field:astronomymapping · IVOA Registryconnector:ivoa_registry@1.0.0
concepts[modality].ivoa_waveband:gamma-raysource · IVOA Registryconnector:ivoa_registry@1.0.0
descriptionsource · IVOA Registryconnector:ivoa_registry@1.0.0rr.resource.res_description
license_textsource · IVOA Registryconnector:ivoa_registry@1.0.0
publication_datesource · IVOA Registryconnector:ivoa_registry@1.0.0
titlesource · IVOA Registryconnector:ivoa_registry@1.0.0rr.resource.res_title
updated_datesource · IVOA Registryconnector:ivoa_registry@1.0.0