Sub-m s-1 upper limits from a deep HARPS-N radial-velocity search for planets orbiting HD 166620 and HD 144579
Vanderburg, A.; Poretti, E.; Sozzetti, A.; Collier Cameron, A.; Wilson, T. G.; Udry, S.; Pepe, F.; Latham, D. W.; Malavolta, L.; Pinamonti, M.; Mortier, A.; Haywood, R. D.; Dumusque, X.; López-Morales, M.; Rice, K.; Buchhave, L. A.; Cosentino, R.; Ghedina, A.; Stalport, M.; John, A. Anna; Fiorenzano, A.; Faria, J. P.; Nicholson, B. A.
United Kingdom, Portugal, Italy, Denmark, Switzerland, United States, Spain, Belgium, Australia
Abstract
Minimizing the impact of stellar variability in radial velocity (RV) measurements is a critical challenge in achieving the 10 cm s-1 precision needed to hunt for Earth twins. Since 2012, a dedicated programme has been underway with HARPS-N, to conduct a blind RV rocky planets search (RPS) around bright stars in the Northern hemisphere. Here we describe the results of a comprehensive search for planetary systems in two RPS targets, HD 166620 and HD 144579. Using wavelength-domain line-profile decorrelation vectors to mitigate the stellar activity and performing a deep search for planetary reflex motions using a trans-dimensional nested sampler, we found no significant planetary signals in the data sets of either of the stars. We validated the results via data-splitting and injection recovery tests. Additionally, we obtained the 95th percentile detection limits on the HARPS-N RVs. We found that the likelihood of finding a low-mass planet increases noticeably across a wide period range when the inherent stellar variability is corrected for using SCALPELSU-vectors. We are able to detect planet signals with Msin i ≤ 1 M⊕ for orbital periods shorter than 10 d. We demonstrate that with our decorrelation technique, we are able to detect signals as low as 54 cm s-1, which brings us closer to the calibration limit of 50 cm s-1 demonstrated by HARPS-N. Therefore, we show that we can push down towards the RV precision required to find Earth analogues using high-precision radial velocity data with novel data-analysis techniques.