Abstract
Context. The detection of exoplanets by direct imaging is an active research topic in astronomy. Even with the coupling of an extreme adaptive-optics system with a coronagraph, it remains challenging due to the very high contrast between the host star and the exoplanets. Aims. The purpose of this paper is to describe a method, named PACO, dedicated to source detection from angular differential imaging data. Given the complexity of the fluctuations of the background in the datasets, involving spatially variant correlations, we aim to show the potential of a processing method that learns the statistical model of the background from the data. Methods. In contrast to existing approaches, the proposed method accounts for spatial correlations in the data. Those correlations and the average stellar speckles are learned locally and jointly to estimate the flux of the (potential) exoplanets. By preventing from subtracting images including the stellar speckles residuals, the photometry is intrinsically preserved. A nonstationary multi-variate Gaussian model of the background is learned. The decision in favor of the presence or the absence of an exoplanet is performed by a binary hypothesis test. Results. The statistical accuracy of the model is assessed using VLT/SPHERE-IRDIS datasets. It is shown to capture the nonstationarity in the data so that a unique threshold can be applied to the detection maps to obtain consistent detection performance at all angular separations. This statistical model makes it possible to directly assess the false alarm rate, probability of detection, photometric and astrometric accuracies without resorting to Monte-Carlo methods. Conclusions. PACO offers appealing characteristics: it is parameter-free and photometrically unbiased. The statistical performance in terms of detection capability, photometric and astrometric accuracies can be straightforwardly assessed. A fast approximate version of the method is also described that can be used to process large amounts of data from exoplanets search surveys.
Highlights
Direct imaging of exoplanets is a recent observational technique (Traub & Oppenheimer 2010) adapted to the observation of young and massive exoplanets
Two exoplanet-searchers are optimized using these instrumental techniques for direct imaging observations: the Spectro-Polarimetry High-contrast Exoplanet REsearch (SPHERE; Beuzit et al 2008) at the Very Large Telescope (VLT) of the European Southern Observatory (ESO) and the Gemini Planet Imager (GPI; Macintosh et al 2014) at the GEMINI observatory
The unsharp filtering applied on the detection maps from TLOCI and KLIP improves their visual quality since areas with large signal-to-noise ratio (S/N) values due to the stellar leakages are largely attenuated, but the detection performance of these algorithms are not significantly improved by this postprocessing
Summary
Direct imaging of exoplanets is a recent observational technique (Traub & Oppenheimer 2010) adapted to the observation of young and massive exoplanets. In the ALOCI algorithm (Currie et al 2012a,b), the data are divided into annuli and processed independently, thereby allowing different linear combinations at different angular separations in the definition of the stellar PSF Another method, called ANDROMEDA (Mugnier et al 2009; Cantalloube et al 2015), forms differences of temporal images to suppress stellar speckles and performs the detection of differential off-axis PSFs (i.e., the signature of an exoplanet in the difference images). MEDUSAE method (Ygouf 2012; Cantalloube 2016) performs an inversion to identify the phase aberrations in a physical model of the coronagraph and deconvolve the images to recover the circumstellar objects by exploiting spectral diversity and regularization terms All of these techniques have several tuning parameters that often require tuning by trial and error and the intervention of an expert, making the optimality difficult to reach. The pre-calibration is dependent on the injected fake exoplanet flux, resulting in a large processing time
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