TESTING REAL-TIME EXPERT SYSTEMS IN EXOPLANETARY MICROLENSING

Abstract

To aid in the search for Extra-Solar planets via Microlensing, the scientific community use Real-Time expert systems to monitor and analyse the incoming observation data. However there are two issues that cause these types of system to not be as efficient as they could otherwise be: the differing filters of the various survey groups and the inability to model in real time. These issues make it more difficult to identify deviations from the lightcurve that may be due to an Extra-Solar planet. Two approaches were taken in order to establish if there existed a more efficient method of using these types of systems; photometric and model convergence. The photometric approach showed a Colour-Magnitude relation between the CTIO and MOA surveys. The method could be extended further to apply to other survey combina- tions, however this would require a change in observational strategy in order to collect sufficient data. It was determined that a weakly correlated, non-zero relation between the two telescopes could be derived under a specific set of circumstances regarding the num- ber of events observed and how many observations were made for each of them. With further investigation and data acquistion it may be possible to define a more accurate relation, as well as relations for other telescope combinations. The model convergence approach produced some insight into how the fitted values of the tE and u0 parameters change over the course of the event, as well as how closely the two are related. The majority of events constrain these parameters to within 95% shortly after the peak of the event. A small minority of events, approximately 10%, remain unconstrained long after the event. It is suggested that the current observing strategy does not fully take in to account the timescales on which the models are constrained, and that the inclusion of such ideas could lead to a more efficient observing strategy.

Date of Award	31 Jul 2012
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Eamonn Kerins (Supervisor)