2021
DOI: 10.1093/mnras/stab182
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A hot mini-Neptune in the radius valley orbiting solar analogue HD 110113

Abstract: We report the discovery of HD 110113 b (TOI-755.01), a transiting mini-Neptune exoplanet on a 2.5-day orbit around the solar-analogue HD 110113 (Teff= 5730K). Using TESS photometry and HARPS radial velocities gathered by the NCORES program, we find HD 110113 b has a radius of 2.05 ± 0.12  R⊕ and a mass of 4.55 ± 0.62  M⊕. The resulting density of $2.90^{+0.75}_{-0.59}$  g cm−3 is significantly lower than would be expected from a pure-rock world; therefore, HD 110113 b must be a mini-Neptune with a significant … Show more

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Cited by 15 publications
(5 citation statements)
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“…Considering this, we performed an additional dynesty fit assuming a 4-planet model and including a Gaussian Process (GP) regression with a quasi-periodic kernel, as formulated in Grunblatt et al (2015), to account for the long-term signal. We modelled simultaneously the RVs and the S-index time series in order to better inform the GP (Langellier et al 2021;Osborn et al 2021), using two independent covariance matrices for each dataset with common GP hyper-parameters except for the amplitude of the covariance matrix, assuming uniform, non-informative priors on all of them. The fit suggests a periodicity longer than in the case of the 5-Keplerian fit, the HARPS-N jitter is significantly improved ( HARPS−N ∼ 1.30 m s −1 ) when including the GP model.…”
Section: Additional Signals In the Rv Datamentioning
confidence: 99%
“…Considering this, we performed an additional dynesty fit assuming a 4-planet model and including a Gaussian Process (GP) regression with a quasi-periodic kernel, as formulated in Grunblatt et al (2015), to account for the long-term signal. We modelled simultaneously the RVs and the S-index time series in order to better inform the GP (Langellier et al 2021;Osborn et al 2021), using two independent covariance matrices for each dataset with common GP hyper-parameters except for the amplitude of the covariance matrix, assuming uniform, non-informative priors on all of them. The fit suggests a periodicity longer than in the case of the 5-Keplerian fit, the HARPS-N jitter is significantly improved ( HARPS−N ∼ 1.30 m s −1 ) when including the GP model.…”
Section: Additional Signals In the Rv Datamentioning
confidence: 99%
“…Considering this, we performed an additional dynesty fit assuming a 4-planet model and including a Gaussian Process (GP) regression with a quasi-periodic kernel, as formulated in Grunblatt et al (2015), to account for the long-term signal. We modelled simultaneously the RVs and the S-index time series in order to better inform the GP (Langellier et al 2021;Osborn et al 2021), using two independent covariance matrices for each dataset with common GP hyper-parameters except for the amplitude of the covariance matrix, assuming uniform, non-informative priors on all of them. The fit suggests a periodicity longer than ∼ 570 d, but the GP model is too flexible to derive a precise period value, considering also that the global RV baseline (∼ 768 d) is comparable with the periodicity of the long-term signal.…”
Section: Additional Signals In the Rv Datamentioning
confidence: 99%
“…A slight variation on the training-GP approach involves modelling the ancillary time-series and the RVs simultaneously, using independent GPs sharing the same covariance function (see e.g., Osborn et al 2021;Suárez Mascareño et al 2020). In this case, both the RVs and the ancillary time-series are used to constrain the GP hyper-parameters, and the functions describing them share exactly the same covariance properties, but they are still independent of each other and have unrelated shapes.…”
Section: Comparison Between Multi-dimensional Gp and Other Approachesmentioning
confidence: 99%