KEYWORDS: Signal detection, Signal to noise ratio, Photon counting, Venus, Planets, Image processing, Sensors, Point spread functions, Electron multiplying charge coupled devices, Exoplanets
Because exoplanets are extremely dim, an electron multiplying charge-coupled device operating in photon counting (PC) mode is necessary to reduce the detector noise level and enable their detection. Typically, PC images are added together as a co-added image before processing. We present a signal detection and estimation technique that works directly with individual PC images. The method is based on the generalized likelihood ratio test (GLRT) and uses a Bernoulli distribution between PC images. The Bernoulli distribution is derived from a stochastic model for the detector, which accurately represents its noise characteristics. We show that our technique outperforms a previously used GLRT method that relies on co-added images under a Gaussian noise assumption and two detection algorithms based on signal-to-noise ratio. Furthermore, our method provides the maximum likelihood estimate of exoplanet intensity and background intensity while doing detection. It can be applied online, so it is possible to stop observations once a specified threshold is reached, providing confidence for the existence (or absence) of planets. As a result, the observation time is efficiently used. In addition to the observation time, the analysis of detection performance introduced in the paper also gives quantitative guidance on the choice of imaging parameters, such as the threshold. Lastly, though our work focuses on the example of detecting point source, the framework is widely applicable.
KEYWORDS: Planets, Signal detection, Venus, Exoplanetary science, Signal to noise ratio, Point spread functions, Photon counting, Telescopes, Coronagraphy, Image processing
A starshade suppresses starlight by a factor of 1011 in the image plane of a telescope, which is crucial for directly imaging Earth-like exoplanets. The state-of-the-art in high-contrast post-processing and signal detection methods was developed specifically for images taken with an internal coronagraph system and focused on the removal of quasi-static speckles. These methods are less useful for starshade images where such speckles are not present. We are dedicated to investigating signal processing methods tailored to work efficiently on starshade images. We describe a signal detection method, the generalized likelihood ratio test (GLRT), for starshade missions and look into three important problems. First, even with the light suppression provided by the starshade, rocky exoplanets are still difficult to detect in reflected light due to their absolute faintness. GLRT can successfully flag these dim planets. Moreover, GLRT provides estimates of the planets’ positions and intensities and the theoretical false alarm rate of the detection. Second, small starshade shape errors, such as a truncated petal tip, can cause artifacts that are hard to distinguish from real planet signals; the detection method can help distinguish planet signals from such artifacts. The third direct imaging problem is that exozodiacal dust degrades detection performance. We develop an iterative GLRT to mitigate the effect of dust on the image. In addition, we provide guidance on how to choose the number of photon counting images to combine into one co-added image before doing detection, which will help utilize the observation time efficiently. All the methods are demonstrated on realistic simulated images.
Exoplanet detection by direct imaging is difficult because the target planets are tens of milliarcseconds away from their host star and many orders of magnitude dimmer, up to 1e10 for Earth-like planets in the habitable zone. An important approach to obtaining that high contrast in a space telescope is via a starshade to suppress the light from the parent star, making the direct imaging possible. Even after suppressing the starlight, the exoplanet signals are still weak. A photon-counting (PC) Electron Multiplying Charged Coupled Device(EMCCD) reduces the noise level and enables the detection of exoplanets. We present here a signal detection and estimation technique working directly with PC images. The method is based on the generalized maximum likelihood ratio test (GLRT) and uses a binomial distribution between PC images. The method can be applied online, so we can stop taking images as soon as we have enough confidence for the lack of or existence of planets. Thus, the observation time is efficiently used.
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
A starshade is a specially designed opaque screen to suppress starlight and remove the effects of diffraction at the edge. The intensity at the pupil plane in the shadow is dark enough to detect Earth-like exoplanets by using direct imaging. At Princeton, we have designed and built a testbed that allows verification of scaled starshade designs whose suppressed shadow is mathematically identical to that of space starshade. The starshade testbed uses a 77.2 m optical propagation distance to realize the flight Fresnel number of 14.5. Here, we present lab result of a revised sample design operating at a flight Fresnel number. We compare the experimental results with simulations that predict the ultimate contrast performance.
Direct imaging using a starshade is a powerful technique for exoplanet detection and characterization. No current post-processing methods are specialized for starshade images and the ones for coronagraph images have not been applied to images produced by a starshade system ( starshade system means the light sources, starshade and telescope). Here, we report on the first step towards adapting these methods for starshade systems. We have built a starshade imaging model. We generate the image based on a simulation of the real astronomical scene and consider the effects of various starshade defects, misalignment, wavefront error, and detector noise. Future work will add the system dynamics of formation flying between the starshade and the telescope. The ultimate goal is to adapt coronagraphic image processing methods for starshade imaging.
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