Structure of Quasar Continuum Emission Regions and Cosmology from Optical and X-Ray Microlensing in Gravitationally Lensed Quasars

Abstract

Quasars are the most energetic objects in the universe. They are thought to be powered by the accretion of gas onto super-massive black holes at the centers of galaxies. The structure of these exotic objects is poorly understood because their central engines cannot be resolved with ordinary telescopes. Gravitational telescopes, however, provide the necessary resolution to study the structure of quasar central engines. This project analyzed the microlensing variability in five gravitationally lensed quasar systems to probe the structure of the continuum emission regions at optical and X-ray wavelengths and make time delay estimates in the systems in which sufficient data were available. The flux of each component of the multiply-imaged quasars was measured in many seasons of ground-based optical imagery. Lightcurves were constructed from the flux measurements, and Monte Carlo methods were used to analyze the microlensing variability in the lightcurves. The results of the Monte Carlo routine were analyzed with Bayesian methods, yielding estimates of the time delays and the sizes of the quasar accretion disks in QJ0158 4325 and HE1104 1805.

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Document Details

Document Type
Technical Report
Publication Date
May 02, 2008
Accession Number
ADA486725

Entities

People

  • Michael E. Eyler

Organizations

  • United States Naval Academy

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Black Holes
  • Bremsstrahlung
  • Charge Coupled Devices
  • Charged Particles
  • Circular Orbits
  • Cosmology
  • Digital Images
  • Electromagnetic Scattering
  • Ground Based
  • Information Science
  • Monte Carlo Method
  • Observatories
  • Probability Distributions
  • Radiation
  • Scattering
  • United States Naval Academy
  • X Rays

Fields of Study

  • Physics

Readers

  • Astronomy/Astrophysics

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Space