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Register a new userA public HARPS radial velocity database corrected for systematic errors
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Added by
Trifon Trifonov Ph.D.
Publication date
2020
fields
Physics
and research's language is
English
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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 numerical mask. Aims. The aim of this study is three-fold: (i) Create easy access to the public HARPS RV data set. (ii) Apply the new public SERVAL pipeline to the spectra, and produce a more precise RV data set. (iii) Check whether the precision of the RVs can be further improved by correcting for small nightly systematic effects. Methods. For each star observed with HARPS, we downloaded the publicly available spectra from the ESO archive and recomputed the RVs with SERVAL. We then computed nightly zero points (NZPs) by averaging the RVs of quiet stars. Results. Analysing the RVs of the most RV-quiet stars, whose RV scatter is < 5 m/s, we find that SERVAL RVs are on average more precise than DRS RVs by a few percent. We find three significant systematic effects, whose magnitude is independent of the software used for the RV derivation: (i) stochastic variations with a magnitude of 1 m/s; (ii) long-term variations, with a magnitude of 1 m/s and a typical timescale of a few weeks; and (iii) 20-30 NZPs significantly deviating by a few m/s. In addition, we find small (< 1 m/s) but significant intra-night drifts in DRS RVs before the 2015 intervention, and in SERVAL RVs after it. We confirm that the fibre exchange in 2015 caused a discontinuous RV jump, which strongly depends on the spectral type of the observed star: from 14 m/s for late F-type stars, to -3 m/s for M dwarfs. Conclusions. Our NZP-corrected SERVAL RVs can be retrieved from a user-friendly, public database. It provides more than 212 000 RVs for about 3000 stars along with many auxiliary information, NZP corrections, various activity indices, and DRS-CCF products.
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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.
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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 remove the activity signals (due to starspots/faculae) from RV observations. Previous efforts focused on carefully filtering out activity signals in time using modeling techniques like Gaussian Process regression (e.g. Haywood et al. 2014). Instead, we systematically remove activity signals using only changes to the average shape of spectral lines, and no information about when the observations were collected. We trained our machine learning models on both simulated data (generated with the SOAP 2.0 software; Dumusque et al. 2014) and observations of the Sun from the HARPS-N Solar Telescope (Dumusque et al. 2015; Phillips et al. 2016; Collier Cameron et al. 2019). We find that these techniques can predict and remove stellar activity from both simulated data (improving RV scatter from 82 cm/s to 3 cm/s) and from more than 600 real observations taken nearly daily over three years with the HARPS-N Solar Telescope (improving the RV scatter from 1.47 m/s to 0.78 m/s, a factor of ~ 1.9 improvement). In the future, these or similar techniques could remove activity signals from observations of stars outside our solar system and eventually help detect habitable-zone Earth-mass exoplanets around Sun-like stars.
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