Efficiently combining α CenA multi-epoch high-contrast imaging data

Application of K-Stacker to the 80 hours NEAR campaign^★

¹ Aix Marseille Univ., CNRS, CNES, LAM, Marseille, France
e-mail: herve.lecoroller@lam.fr
² Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
³ Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
⁴ Department of Astronomy and Steward Observatory, University of Arizona, Tucson, AZ, USA
⁵ European Southern Observatory, Garching bei München, Germany
⁶ Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange, Nice, France
⁷ Univ. Grenoble-Alpes, CNRS, IPAG, 38000 Grenoble, France
⁸ Max-Planck-Institut für Astronomie, Königstuhl 17, 69117 Heidelberg, Germany
⁹ Université Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM, 91191 Gif-sur-Yvette, France

Received: 17 March 2022
Accepted: 13 July 2022

Abstract

Context. Keplerian-Stacker is an algorithm capable of combining multiple observations acquired at different epochs by taking into account the orbital motion of a potential planet present in the images to boost the ultimate detection limit. In 2019, a total of 100 h of observation was allocated to Very Large Telescope (VLT) Spectrometer and Imager for the mid-infrared (VISIR) instrument for the New Earths in the α Centauri Region (NEAR) survey, a collaboration between European Southern Observatory (ESO) and Breakthrough Initiatives, to search for low mass planets in the habitable zone of the α Cen AB binary system. A weak signal (S/N ~ 3) was reported around α Cen A, at a separation of ≃ 1.1 au, corresponding to the habitable zone.

Aims. Our study is aimed at determining whether K-Stacker is also capable of detecting the low-mass planet candidate with similar orbital parameters, which was previously found by the NEAR team. We also aim to search for additional potential candidates around a Cen A by utilizing the orbital motion to boost the signal and by generally placing stronger constraints on the presence of other planets in the system.

Methods. We re-analysed the NEAR data using K-Stacker. This algorithm is a brute-force method that is equipped to find planets in observational time series and to constrain their orbital parameters, even if they have remained undetected in a single epoch.

Results. We scanned a total of about 3.5 × 10⁵ independent orbits, among which close to 15% correspond to fast-moving orbits on which planets cannot be detected without taking into account the orbital motion. We found only a single planet candidate that matches the C1 detection reported in Wagner et al. (2021, Nat. Commun., 12, 922). However, since this constitutes a re-analysis of the same data set, more observations will be necessary to confirm that C1 is indeed a planet and not a disk or other data artifact. Despite the significant amount of time spent on this target, the orbit of this candidate remains poorly constrained due to these observations being closely distributed across 34 days. We argue that future single-target deep surveys would benefit from a K-Stacker based strategy, where the observations would be split over a significant part of the expected orbital period to better constrain the orbital parameters.

Conclusions. This application of K-Stacker to high-contrast imaging data in the mid-infrared demonstrates the capability of this algorithm in aiding the search for Earth-like planets in the habitable zone of the nearest stars with future instruments of the E-ELT, such as METIS.