A re-analysis of exomoon candidate MOA-2011-BLG-262lb using the Besancon Galactic Model

UoM administered thesis: Master of Science by Research

  • Authors:
  • Parvin Mansour

Abstract

Gravitational microlensing is the bending of star light due to gravitational influence of a massive compact object, known as the lens, along the line of sight. The presence of any planet orbiting the lens can be detected via the microlensing method. Due to the fact that it does not rely on detection of photon from the star or the planet, this method provides a powerful tool for detecting free floating planets and cool exoplanets orbiting a wide range of stars with distances of order of several kpc. The physical characteristics of the lens system can be determined by constructing a model that matches with the observed data. Unfortunately, typical microlensing models suffer from a two fold degeneracy, which means that the mass and distance of the lens cannot be disentangled. Finding the best parameters set that provide a good description of the observed microlensing light curve is a challenging task. Different model fits can produce similar light curves with reasonable agreement with the observation, therefore it is essential to be able to compute the probability density of different model fits. We developed a software, using the Besan¸con population synthesis model of the Galaxy, that can predict the probability density of different microlensing event model fits. We used this software to compute the probability density of two models describing the microlensing event MOA-2011-BLG-262-lb, a free floating planet-moon system and a star-planet system with a super Earth orbiting a star. We calculated the relative posterior probability of both model fits by incorporating selection functions for the Einstein radius crossing time, relative proper motion, source apparent magnitude and χ 2 from the MOA-2011-BLG-262-lb event and found that the ratio of the planet-moon model posterior probability to that of the star-planet model is in order of 10−10 .

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Original languageEnglish
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Award date1 Aug 2018