SIMBAD references

2019MNRAS.490.4770K - Mon. Not. R. Astron. Soc., 490, 4770-4777 (2019/December-3)

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


Abstract (from CDS):

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 develop 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 γ-ray instrument. 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 out of 573 blazar candidates of uncertain type listed in the LAT 4-year Source Catalog.

Abstract Copyright: © 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society

Journal keyword(s): methods: statistical - galaxies: active - BL Lacertae objects: general - gamma-rays: galaxies

VizieR on-line data: <Available at CDS (J/MNRAS/490/4770): table1.dat>

Status at CDS : All or part of tables of objects will be ingested in SIMBAD with priority 1.

Simbad objects: 567

goto Full paper

goto View the references in ADS

To bookmark this query, right click on this link: simbad:2019MNRAS.490.4770K and select 'bookmark this link' or equivalent in the popup menu


© Université de Strasbourg/CNRS

    • Contact