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We aim at searching for exoplanets on the whole ESO/VLT-SPHERE archive withimproved and unsupervised data analysis algorithm that could allow to detectmassive giant planets at 5 au. To prepare, test and optimize our approach, wegathered a sample of twenty four solar-type stars observed with SPHERE usingangular and spectral differential imaging modes. We use PACO, a new generationalgorithm recently developed, that has been shown to outperform classicalmethods. We also improve the SPHERE pre-reduction pipeline, and optimize theoutputs of PACO to enhance the detection performance. We develop custom builtspectral prior libraries to optimize the detection capability of the ASDI modefor both IRDIS and IFS. Compared to previous works conducted with moreclassical algorithms than PACO, the contrast limits we derived are morereliable and significantly better, especially at short angular separationswhere a gain by a factor ten is obtained between 0.2 and 0.5 arcsec. Under goodobserving conditions, planets down to 5 MJup, orbiting at 5 au could bedetected around stars within 60 parsec. We identified two exoplanet candidatesthat require follow-up to test for common proper motion. In this work, wedemonstrated on a small sample the benefits of PACO in terms of achievablecontrast and of control of the confidence levels. Besides, we have developedcustom tools to take full benefits of this algorithm and to quantity the totalerror budget on the estimated astrometry and photometry. This work paves theway towards an end-to-end, homogeneous, and unsupervised massive re-reductionof archival direct imaging surveys in the quest of new exoJupiters.

Preparing an unsupervised massive analysis of SPHERE high contrast data with the PACO algorithm