SIMBAD references

2014ApJ...782...41D - Astrophys. J., 782, 41 (2014/February-2)

Search for gamma-ray-emitting active galactic nuclei in the Fermi-LAT unassociated sample using machine learning.


Abstract (from CDS):

The second Fermi-LAT source catalog (2FGL) is the deepest all-sky survey available in the gamma-ray band. It contains 1873 sources, of which 576 remain unassociated. Machine-learning algorithms can be trained on the gamma-ray properties of known active galactic nuclei (AGNs) to find objects with AGN-like properties in the unassociated sample. This analysis finds 231 high-confidence AGN candidates, with increased robustness provided by intersecting two complementary algorithms. A method to estimate the performance of the classification algorithm is also presented, that takes into account the differences between associated and unassociated gamma-ray sources. Follow-up observations targeting AGN candidates, or studies of multiwavelength archival data, will reduce the number of unassociated gamma-ray sources and contribute to a more complete characterization of the population of gamma-ray emitting AGNs.

Abstract Copyright:

Journal keyword(s): catalogs - galaxies: active - gamma rays: galaxies - methods: statistical

VizieR on-line data: <Available at CDS (J/ApJ/782/41): table3.dat>

Simbad objects: 235

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