K-Stacker: Keplerian image recombination for the direct detection of exoplanets
LESIA, Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Univ. Paris Diderot, Sorbonne Paris Cité,
5 place Jules Janssen,
2 Aix-Marseille Université, CNRS, CNES, LAM, Marseille, France
3 Aix-Marseille Université, CNRS, OHP (Observatoire de Haute Provence), Institut Pythéas UMS 3470, 04870 Saint-Michel-l’Observatoire, France
4 ONERA – The French Aerospace Lab BP72 – 29 avenue de la Division Leclerc, 92322 Chatillon Cedex, France
5 Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LAPP, 74000 Annecy, France
Accepted: 30 March 2018
Context. Angular differential imaging (ADI) takes advantage of the field rotation naturally induced by altitude-azimuth mounts to reduce static speckle noise. Used with facilities like SPHERE at the VLT, this technique allows one to achieve contrast ratios of 10−6. The ADI method, however, intrinsically limits the useful exposure time on a given target (to about 1–2 h per night). Detecting fainter exoplanets requires the combination of multiple observations acquired on different nights, potentially spread over several weeks or months, but the unknown orbital motion of the planet makes it particularly dififcult to properly combine all observations. In the near future, with the upcoming generation of Extremely Large Telescopes (ELTs) with increased resolution, the orbital motion may even become a problem on a single night.
Aims. We present a proof of concept for a new algorithm which can be used to detect exoplanets in high-contrast images. The algorithm properly combines multiple observations acquired during different nights, taking into account the orbital motion of the planet.
Methods. We simulate SPHERE/IRDIS time series of observations in which we blindly inject planets on random orbits, at random levels of signal-to-noise ratio (S/N), below the detection limit (down to S∕N ≃ 1.5). We then use an optimization algorithm to “guess” the orbital parameters, and take into account the orbital motion to properly recombine the different images and eventually detect the planets.
Results. We show that an optimization algorithm can indeed be used to find undetected planets in temporal sequences of images, even if they are spread over orbital time scales. As expected, the typical gain in S/N is √n, n being the number of observations combined. We find that the K-Stacker algorithm is able de-orbit and combine the images to reach a level of performance similar to what could be expected if the planet was not moving. We find recovery rates of ≃ 50% at S∕N = 5. We also find that the algorithm is able to determine the position of the planet in individual frames at one pixel precision, even despite the fact that the planet itself is below the detection limit in each frame.
Conclusions. Our simulations show that K-Stacker can be used to detect planets at very low S/N level, down to ≃2 in individual frames, for series of ten images. This could be used to increase the contrast limit of current exoplanet imaging instruments and to discover fainter bodies. We also suggest that the ability of K-Stacker to determine the position of the planet in every image of the time series could be used as part of a new observing strategy in which long exposures would be broken into shorter ones spread over months. This could make it possible to determine the orbital parameters of a planet without multiple high-S/N (>5) detections.
Key words: techniques: image processing / planets and satellites: detection
© ESO 2018
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0;), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.