Abstract
High contrast imaging from space relies on coronagraphs to limit diffraction and a wavefront control systems to compensate for imperfections in both the telescope optics and the coronagraph. The extreme contrast required (up to 10(-10) for terrestrial planets) puts severe requirements on the wavefront control system, as the achievable contrast is limited by the quality of the wavefront. This paper presents a general closed loop correction algorithm for high contrast imaging coronagraphs by minimizing the energy in a predefined region in the image where terrestrial planets could be found. The estimation part of the algorithm reconstructs the complex field in the image plane using phase diversity caused by the deformable mirror. This method has been shown to achieve faster and better correction than classical speckle nulling.
Highlights
IntroductionThe problem of direct detection of exoplanets requires high-contrast imaging of a dim point source (the planet) appearing adjacent to a much brighter source (the star)
The problem of direct detection of exoplanets requires high-contrast imaging of a dim point source appearing adjacent to a much brighter source
In this note we describe an algorithm using a single deformable mirror (DM) for estimating and correcting static wavefront error in a coronagraphic imaging system
Summary
The problem of direct detection of exoplanets requires high-contrast imaging of a dim point source (the planet) appearing adjacent to a much brighter source (the star). [7] for correcting the field in a classical Lyot coronagraph which in turn has its origin in the linear solution of the ‘dark hole algorithm’ developed by Malbet et al [8]. We have generalized these approaches by incorporating the full, nonlinear expression for the aberrations, including scattered light and the nominal diffraction of the system, and by formulating the correction in a manner applicable to a wide variety of high-contrast imaging systems. 2. Energy Minimization: a closed-loop correction algorithm for high contrast imaging systems. It is important to note that while we present a complete closed-loop algorithm, these two steps are functionally independent and could be implemented with other companion approaches
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