Utilizing Swift-XRT Data to Identify Source Classes in Fermi Unassociated Objects
Open Access
Author:
Pryal, Matthew
Area of Honors:
Astronomy and Astrophysics
Degree:
Bachelor of Science
Document Type:
Thesis
Thesis Supervisors:
Abraham David Falcone, Thesis Supervisor Alexander Wolszczan, Thesis Honors Advisor
Keywords:
Fermi Swift X-rays Gamma-rays Data Mining Machine Learning Astrostatistics
Abstract:
The Large Area Telescope on board the Fermi Gamma-Ray Space Telescope has revolutionized the detection and identification of gamma-ray emitting astrophysical objects since the launch of Fermi in 2008. Gamma-rays are the highest energy photons observed in the universe and our understanding of objects that emit gamma-rays is essential to our understanding of physics at its greatest limits. The 1FGL, 2FGL, and 3FGL Fermi catalogs outline the identification of over 2000 gamma-ray emitting objects such as blazars and pulsars, but there still remains many unassociated objects in the Fermi catalogs. In this thesis we attempt to statistically show associations of these Fermi unassociated objects in the 1FGL and 2FGL catalogs by utilizing data mining and machine learning techniques to find distinct separations in the properties of known blazars and pulsars. Our analysis is unique in that we will be utilizing known X-ray fluxes for the unassociated objects observed by the Swift X-ray Telescope in addition to the gamma-ray properties available in the Fermi catalogs. Our analysis shows distinct separations in the gamma-ray properties of known blazars and pulsars when compared to X-ray flux and we were able to suggest possible associations to 136 previously unassociated Fermi objects.
