Machine-learning classifiers for Fermi AGN
| dc.contributor.author | Hassan, T. | |
| dc.contributor.author | Mirabal, Nestor | |
| dc.contributor.author | Contreras, J. L. | |
| dc.contributor.author | Oya, I. | |
| dc.date.accessioned | 2020-09-03T16:26:36Z | |
| dc.date.available | 2020-09-03T16:26:36Z | |
| dc.date.issued | 2012-12-11 | |
| dc.description.abstract | The Fermi Gamma-ray Space Telescope is generating the most detailed map of the gamma-ray sky. While tremendously successful, approximately 25% of all associated Fermi extragalactic sources in the Second Fermi LAT Catalog (2FGL) are listed as active galactic nuclei (AGN) of uncertain type. Most of these are suspected blazar candidates without a conclusive optical spectrum or lacking spectroscopic observations. Here, we explore the use of machine-learning algorithms – Random Forests and Support Vector Machines – to predict specific AGN subclass based on observed gamma-ray properties. | en_US |
| dc.description.sponsorship | The authors acknowledge the support of the Spanish MINECO under project FPA2010-22056-C06-06 and the German Ministry for Education and Research (BMBF). N.M. acknowledges support from the Spanish government. through a Ram´on y Cajal fellowship. We also thank the referee for useful suggestions and comments on the manuscript. | en_US |
| dc.description.uri | https://aip.scitation.org/doi/abs/10.1063/1.4772356 | en_US |
| dc.format.extent | 7 pages | en_US |
| dc.genre | conference papers and proceedings preprints | en_US |
| dc.identifier | doi:10.13016/m21qaa-8bnh | |
| dc.identifier.citation | T. Hassan, N. Mirabal, I. Oya, and J. L. Contreras, Machine-learning classifiers for Fermi AGN, AIP Conference Proceedings 1505, 701 (2012); https://doi.org/10.1063/1.4772356 | en_US |
| dc.identifier.uri | https://doi.org/10.1063/1.4772356 | |
| dc.identifier.uri | http://hdl.handle.net/11603/19577 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | AIP publishing | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Physics Department Collection | |
| dc.relation.ispartof | UMBC Joint Center for Earth Systems Technology (JCET) | |
| dc.relation.ispartof | UMBC Center for Space Sciences and Technology (CSST) / Center for Research and Exploration in Space Sciences & Technology II (CRSST II) | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.rights | © 2012 AIP Publishing LLC | |
| dc.title | Machine-learning classifiers for Fermi AGN | en_US |
| dc.type | Text | en_US |