VizieR Online Data Catalog: Gamma-ray AGN type determination (Hassan+, 2013)
Abstract
In this paper, we employ Support Vector Machines (SVMs) and Random Forest (RF) that embody two of the most robust supervised learning algorithms available today. We are interested in building classifiers that can distinguish between two AGN classes: BL Lacs and FSRQs. In the 2FGL, there is a total set of 1074 identified/associated AGN objects with the following labels: 'bzb' (BL Lacs), 'bzq' (FSRQs), 'agn' (other non-blazar AGN) and 'agu' (active galaxies of uncertain type). From this global set, we group the identified/associated blazars ('bzb' and 'bzq' labels) as the training/testing set of our algorithms.
(2 data files).- Publication:
-
VizieR Online Data Catalog
- Pub Date:
- November 2013
- Bibcode:
- 2013yCat..74280220H
- Keywords:
-
- Gamma rays;
- Active gal. nuclei