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Simulation of Realistic Images and E...

hu, Mengya.

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Simulation of Realistic Images and Exoplanet Detection for Starshade Missions.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Simulation of Realistic Images and Exoplanet Detection for Starshade Missions./
作者:	hu, Mengya.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	125 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
Contained By:	Dissertations Abstracts International82-12B.
標題:	Aerospace engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28490686
ISBN:	9798738648656

Simulation of Realistic Images and Exoplanet Detection for Starshade Missions.
hu, Mengya.

Simulation of Realistic Images and Exoplanet Detection for Starshade Missions. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 125 p.

Source: Dissertations Abstracts International, Volume: 82-12, Section: B.

Thesis (Ph.D.)--Princeton University, 2021.

This item must not be sold to any third party vendors.

A starshade suppresses starlight by a factor of 1E11 in the image plane of a telescope, which is crucial for directly imaging Earth-like exoplanets. The state of the art in high-contrast signal detection methods was developed specifically for coronagraph images and focuses on the removal of quasi-static speckles. These methods are less useful for starshade images where such speckles are not present. This work is dedicated to investigating signal detection tailored to starshade images. I begin with the first step towards the investigation: realistic starshade image simulation. The simulation considers factors such as starshade defects and detector noise. Then, signal detection methods are presented.Due to the absolute faintness of Earth-like planets, an Electron Multiplying Charged Coupled Device operating in photon counting (PC) mode is used. Typically, PC images are added together as a co-added image before processing. Therefore, I first introduce a detection method based on a generalized likelihood ratio test (GLRT) for co-added images under the Gaussian assumption. I also extend the method to mitigate the effect of exozodiacal dust. Then, I improve the method by working directly with individual PC images using a Bernoulli distribution. The Bernoulli distribution is derived from a stochastic model for the detector, which accurately represents its noise characteristics. I show that my techniques outperform a popular detection algorithm based on signal to noise ratio. Besides successfully flagging the dim planets, my methods provide the maximum likelihood estimate of exoplanet intensity and background intensity while doing detection. Moreover, my methods can help distinguish planet signals from artifacts caused by starshade defects. It can also guide stopping observations early, providing confidence for the existence (or absence) of planets. As a result, the observation time is efficiently used. Besides the observation time, the analysis of detection performance introduced in the thesis also gives quantitative guidance on the choice of imaging parameters, such as the threshold for PC mode. Last but not the least, though this work focuses on the example of detecting point sources in starshade images, the framework is widely applicable. All the methods are demonstrated on realistic simulated images.

ISBN: 9798738648656Subjects--Topical Terms:

1002622
Aerospace engineering.
Subjects--Index Terms:

Exoplanet

Simulation of Realistic Images and Exoplanet Detection for Starshade Missions.
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520 $a A starshade suppresses starlight by a factor of 1E11 in the image plane of a telescope, which is crucial for directly imaging Earth-like exoplanets. The state of the art in high-contrast signal detection methods was developed specifically for coronagraph images and focuses on the removal of quasi-static speckles. These methods are less useful for starshade images where such speckles are not present. This work is dedicated to investigating signal detection tailored to starshade images. I begin with the first step towards the investigation: realistic starshade image simulation. The simulation considers factors such as starshade defects and detector noise. Then, signal detection methods are presented.Due to the absolute faintness of Earth-like planets, an Electron Multiplying Charged Coupled Device operating in photon counting (PC) mode is used. Typically, PC images are added together as a co-added image before processing. Therefore, I first introduce a detection method based on a generalized likelihood ratio test (GLRT) for co-added images under the Gaussian assumption. I also extend the method to mitigate the effect of exozodiacal dust. Then, I improve the method by working directly with individual PC images using a Bernoulli distribution. The Bernoulli distribution is derived from a stochastic model for the detector, which accurately represents its noise characteristics. I show that my techniques outperform a popular detection algorithm based on signal to noise ratio. Besides successfully flagging the dim planets, my methods provide the maximum likelihood estimate of exoplanet intensity and background intensity while doing detection. Moreover, my methods can help distinguish planet signals from artifacts caused by starshade defects. It can also guide stopping observations early, providing confidence for the existence (or absence) of planets. As a result, the observation time is efficiently used. Besides the observation time, the analysis of detection performance introduced in the thesis also gives quantitative guidance on the choice of imaging parameters, such as the threshold for PC mode. Last but not the least, though this work focuses on the example of detecting point sources in starshade images, the framework is widely applicable. All the methods are demonstrated on realistic simulated images.
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