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We present observations with the planet finder SPHERE of a selected sample of the most promising radial velocity (RV) companions for high-contrast imaging. Using a Monte Carlo simulation to explore all the possible inclinations of the orbit of wide RV companions, we identified the systems with companions that could potentially be detected with SPHERE. We found the most favorable RV systems to observe are : HD,142, GJ,676, HD,39091, HIP,70849, and HD,30177 and carried out observations of these systems during SPHERE Guaranteed Time Observing (GTO). To reduce the intensity of the starlight and reveal faint companions, we used Principle Component Analysis (PCA) algorithms alongside angular and spectral differential imaging. We injected synthetic planets with known flux to evaluate the self-subtraction caused by our data reduction and to determine the 5$sigma$ contrast in the J band $vs$ separation for our reduced images. We estimated the upper limit on detectable companion mass around the selected stars from the contrast plot obtained from our data reduction. Although our observations enabled contrasts larger than 15 mag at a few tenths of arcsec from the host stars, we detected no planets. However, we were able to set upper mass limits around the stars using AMES-COND evolutionary models. We can exclude the presence of companions more massive than 25-28 MJup around these stars, confirming the substellar nature of these RV companions.
RefPlanets is a guaranteed time observation (GTO) programme that uses the Zurich IMaging POLarimeter (ZIMPOL) of SPHERE/VLT for a blind search for exoplanets in wavelengths from 600-900 nm. The goals of this study are the characterization of the unpr
SPHERE (Beuzit et al,. 2019) has now been in operation at the VLT for more than 5 years, demonstrating a high level of performance. SPHERE has produced outstanding results using a variety of operating modes, primarily in the field of direct imaging o
Context. The HARPS spectrograph provides state-of-the-art stellar radial velocity (RV) measurements with a precision down to 1 m/s. The spectra are extracted with a dedicated data-reduction software (DRS) and the RVs are computed by CCF with a numeri
Recent high-contrast imaging surveys, looking for planets in young, nearby systems showed evidence of a small number of giant planets at relatively large separation beyond typically 20 au where those surveys are the most sensitive. Access to smaller
Exoplanet detection with precise radial velocity (RV) observations is currently limited by spurious RV signals introduced by stellar activity. We show that machine learning techniques such as linear regression and neural networks can effectively remo