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Astronomy & Astrophysics, (635), p. A13, 2020

DOI: 10.1051/0004-6361/201936389

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Decoding the radial velocity variations of HD 41248 with ESPRESSO

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Data provided by SHERPA/RoMEO

Abstract

Context. Twenty-four years after the discoveries of the first exoplanets, the radial-velocity (RV) method is still one of the most productive techniques to detect and confirm exoplanets. But stellar magnetic activity can induce RV variations large enough to make it difficult to disentangle planet signals from the stellar noise. In this context, HD 41248 is an interesting planet-host candidate, with RV observations plagued by activity-induced signals. Aims. We report on ESPRESSO observations of HD 41248 and analyse them together with previous observations from HARPS with the goal of evaluating the presence of orbiting planets. Methods. Using different noise models within a general Bayesian framework designed for planet detection in RV data, we test the significance of the various signals present in the HD 41248 dataset. We use Gaussian processes as well as a first-order moving average component to try to correct for activity-induced signals. At the same time, we analyse photometry from the TESS mission, searching for transits and rotational modulation in the light curve. Results. The number of significantly detected Keplerian signals depends on the noise model employed, which can range from 0 with the Gaussian process model to 3 with a white noise model. We find that the Gaussian process alone can explain the RV data while allowing for the stellar rotation period and active region evolution timescale to be constrained. The rotation period estimated from the RVs agrees with the value determined from the TESS light curve. Conclusions. Based on the data that is currently available, we conclude that the RV variations of HD 41248 can be explained by stellar activity (using the Gaussian process model) in line with the evidence from activity indicators and the TESS photometry.

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