Statistics – Applications
Scientific paper
Dec 2001
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001phdt........10w&link_type=abstract
Thesis (PhD). PRINCETON UNIVERSITY, Source DAI-B 62/06, p. 2759, Dec 2001, 172 pages.
Statistics
Applications
Scientific paper
Various aspects of Difference Image Analysis (DIA) are investigated in the context of crowded field photometry and microlensing. I explore the range of applications of the Alard & Lupton linear kernel decomposition algorithm to challenging observational projects. In case of quadruply lensed quasar QSO 2237+0305 I demonstrated that the method optimized for crowded fields performs surprisingly well in one of the emptiest fields of the sky. The achieved level of reliability allowed me to implement a real time monitoring system, which provided unprecedented coverage of several microlensing events in this unique object. The search for variable stars in a globular cluster M5 showed that the method handles well strong gradients of stellar number density. I present my own implementation of the Alard & Lupton method for very large data sets of the OGLE-II project. The error distribution in measurements returned by my photometric pipeline is nearly Gaussian and the amplitude of noise is reduced typically by a factor of 2-3 compared to standard DoPhot based pipeline in OGLE. As a result, the detection sensitivity to microlensing is much improved and for the first time in OGLE-II a fully algorithmic selection of candidate events is possible. Stars with detectable proper motions are serendipitously discovered with image subtraction photometry. The interpretation and quantitative analysis of this effect is presented with the emphasis on the future prospects of constructing complete samples of proper motion stars in dense stellar fields, like the Galactic bulge region.
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