ES-03-0020

Preparing an unsupervised massive analysis of SPHERE high contrast data with the PACO algorithm. Optimizations and benchmarking on a sample of 24 solar-type stars

Antoine Chomez, Anne-Marie Lagrange, Philippe Delorme, Maud Langlois, Gael Chauvin, Olivier Flasseur, Jules Dallant, Florian Philipot, Stephane Bergeon, Damien Albert, Nadege Meunier, Pascal Rubini

Despite tremendous progress in the detection and characterisation of extrasolar planetary systems in the last 25 years, we have not found Solar System analogues. In particular, Jupiter-like planets are barely detectable beyond 5 au with indirect technics and are still out of reach of direct imaging. While more performant instruments will allow detecting lighter and closer planets, improved data reduction algorithm can already allows us to probe in this region.
We aim at searching for exoplanets on the whole ESO/VLT-SPHERE archive with improved, unsupervised, data analysis algorithms that could allow to detect massive giant planets at 5 au . To prepare, test and optimize our approach, we gathered a sample of twenty four solar-type stars observed with SPHERE using angular and spectral differential imaging modes.
We use PACO, a new generation algorithm recently developed, that has been shown to outperform classical method, together with custom build spectral template libraries to optimize the detection capability of the ASDI mode for both IRDIS and IFS. Compared to previous works conducted with more classical algorithms than PACO, the contrast limits we derived are more reliable and significantly better with a gain by a factor ten between 0.2 and 0.5 arcsec.
This work paves the way towards an end-to-end, homogeneous, and unsupervised massive re-reduction of archival direct imaging surveys in the quest of new exoJupiters. An update on current surveys will also be provided.