Abstract
Context. The detection of exoplanets by direct imaging is very challenging. It requires an extreme adaptive-optics (AO) system and a coronagraph as well as suitable observing strategies. In angular differential imaging, the signal-to-noise ratio is improved by combining several observations. Aims. Due to the evolution of the observation conditions and of the AO correction, the quality of the observations may vary significantly during the observing sequence. It is common practice to reject images of comparatively poor quality. We aim to decipher when this selection should be performed and what its impact on detection performance is. Methods. Rather than discarding a full image, we study the local fluctuations of the signal at each frame and derive weighting maps for each frame. These fluctuations are modeled locally directly from the data through the spatio-temporal covariance of small image patches. The weights derived from the temporal variances can be used to improve the robustness of the detection step and reduce estimation errors of both the astrometry and photometry. The impact of bad frames can be analyzed by statistically characterizing the detection and estimation performance. Results. When used together with a modeling of the spatial covariances (PACO algorithm), these weights improve the robustness of the detection method. Conclusions. The spatio-temporal modeling of the background fluctuations provides a way to exploit all acquired frames. In the case of bad frames, areas with larger fluctuations are discarded by a weighting strategy and do not corrupt the detection map or the astrometric and photometric estimations. Other areas of better quality are preserved and are included to detect and characterize sources.
Highlights
Direct imaging from the Earth is a method of choice for the detection and characterization of exoplanets (Traub & Oppenheimer 2010)
The most efficient observation strategy is based on angular differential imaging (ADI, see Marois et al 2006) which consists of tracking the observation target over time
Robust estimation of photometry and astrometry In Appendix B, we prove that the Gaussian scale mixture (GSM) model considered in this paper leads to robust estimates of the background statistics and of the flux of a point source in the sense that the estimates remain bounded when a frame takes arbitrarily large values
Summary
Direct imaging from the Earth is a method of choice for the detection and characterization of exoplanets (Traub & Oppenheimer 2010). The detection performance and the achievable contrast are strongly dependent on both the total parallactic rotation of the field of view and the quality of the observations often impacted by several artifacts These artifacts can be spatially localized (e.g., in case of defective pixels) or can impact a larger fraction of the field of view in the form of large fluctuations when a decentering of the coronagraph or a sudden degradation of the adaptive optics (AO) correction occurs (e.g., a low-wind effect, Sauvage et al 2015; Milli et al 2018). We show that by preventing the suppression of bad frames, the automatic local weighting of the images improves the achievable contrast on the whole field of view
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