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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01bk128d296
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dc.contributor.advisorKasdin, N. Jen_US
dc.contributor.authorYoung, Elizabeth Jensenen_US
dc.contributor.otherMechanical and Aerospace Engineering Departmenten_US
dc.date.accessioned2015-12-08T15:23:55Z-
dc.date.available2017-11-24T09:05:19Z-
dc.date.issued2015en_US
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01bk128d296-
dc.description.abstractThe first successful direct image of an exoplanet was taken in 2008. This field is rapidly advancing in a quest to image Earth-like planets and look for signs of life. All direct imaging techniques use a coronagraph to block the bright starlight and reveal faint companions nearby. A significant challenge in the field is the fact that quasi-static speckles and planets appear to be similar within an image. However, speckles are derived from a single coherent source (the star), and are incoherent with the light from a planet. Therefore, the speckle pattern in an image can be changed, while leaving any planet light unaffected, by moving a deformable mirror within the coronagraphic system. This dissertation presents a technique to analyze a series of images containing different speckle patterns in order to confidently identify planets. One key difficulty in using existing techniques to analyze such images is that they rely on having a known or knowable background in order to extract the planet(s). Instead, we focus on simultaneously estimating the unknown planet and background intensities. Using these estimates, we present a Bayesian analysis in order to assess the evidence towards the existence of a planet. We explore three approaches to calculate the required estimates, namely stacking all images together, estimating the parameters in every image individually, and concurrently estimating a single planet intensity but multiple background intensities for each image. For each approach, we demonstrate how to select a detection threshold, number of images, and overall integration time to match desired probabilities of false alarms and missed detections. We compare the three approaches using both theoretical analysis and simulation results. We find that stacking images provides the greatest planet-detection capability. However, the approach of concurrently estimating the background in each image shows the potential to be improved through technical modifications, and already performs similarly to stacking in some cases. Finally, we use laboratory results to demonstrate that speckle patterns can be changed in the ways that we assumed.en_US
dc.language.isoenen_US
dc.publisherPrinceton, NJ : Princeton Universityen_US
dc.relation.isformatofThe Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog: http://catalog.princeton.edu/en_US
dc.subjectCoronagraphsen_US
dc.subjectDirect Imagingen_US
dc.subjectExoplanetsen_US
dc.subjectHigh Contrast Imagingen_US
dc.subjectSpecklesen_US
dc.subject.classificationAerospace engineeringen_US
dc.subject.classificationAstronomyen_US
dc.titlePlanet identification for speckle-limited coronagraphic imagesen_US
dc.typeAcademic dissertations (Ph.D.)en_US
pu.projectgrantnumber690-2143en_US
pu.embargo.terms2017-11-24en_US
Appears in Collections:Mechanical and Aerospace Engineering

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