Image processing methods for exoplanets detection and characterization in starshade observations

Mengya (Mia) Hu; Anthony Harness; N. Jeremy Kasdin

doi:10.1117/12.2312091

16 July 2018 Image processing methods for exoplanets detection and characterization in starshade observations

Mengya (Mia) Hu, Anthony Harness, N. Jeremy Kasdin

Proceedings Volume 10698, Space Telescopes and Instrumentation 2018: Optical, Infrared, and Millimeter Wave; 106985K (2018) https://doi.org/10.1117/12.2312091
Event: SPIE Astronomical Telescopes + Instrumentation, 2018, Austin, Texas, United States

Abstract

A starshade is a promising instrument for the direct imaging and characterization of exoplanets. However, even with a starshade, exoplanets are difficult to detect because detector noise, starshade defects, and misalignment (dynamics of the starshade system) degrade the signal to noise ratio (SNR) and contrast. No image processing methods have been specialized for images produced by a starshade system (simply referred as starshade images later). In this paper, we present a method, based on the generalized likelihood ratio test (GLRT), to detect and characterize planets from a single starshade image or multiple starshade images. This paper describes the GLRT model and its preliminary results for simulated images with starshade shape error, dynamics, detector noise and starshade rotation considered. The planets are detected with low false alarm rate, and planet positions are accurately estimated, and planet intensities are reasonably estimated. Thus, it demonstrates great potential as an acute and robust detection method for starshade images

Citation Download Citation

Mengya (Mia) Hu, Anthony Harness, and N. Jeremy Kasdin "Image processing methods for exoplanets detection and characterization in starshade observations", Proc. SPIE 10698, Space Telescopes and Instrumentation 2018: Optical, Infrared, and Millimeter Wave, 106985K (16 July 2018); https://doi.org/10.1117/12.2312091

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