A candidate short-period sub-Earth orbiting Proxima Centauri

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Abstract

Context. Proxima Centauri is the closest star to the Sun. This small, low-mass, mid M dwarf is known to host an Earth-mass exoplanet with an orbital period of 11.2 days within the habitable zone, as well as a long-period planet candidate with an orbital period of close to 5 yr.

Aims. We report on the analysis of a large set of observations taken with the ESPRESSO spectrograph at the VLT aimed at a thorough evaluation of the presence of a third low-mass planetary companion, which started emerging during a previous campaign.

Methods. Radial velocities (RVs) were calculated using both a cross-correlation function (CCF) and a template matching approach. The RV analysis includes a component to model Proxima’s activity using a Gaussian process (GP). We use the CCF’s full width at half maximum to help constrain the GP, and we study other simultaneous observables as activity indicators in order to assess the nature of any potential RV signals.

Results. We detect a signal at 5.12 ± 0.04 days with a semi-amplitude of 39 ± 7 cm s ⁻¹. The analysis of subsets of the ESPRESSO data, the activity indicators, and chromatic RVs suggest that this signal is not caused by stellar variability but instead by a planetary companion with a minimum mass of 0.26 ± 0.05 M _⊕ (about twice the mass of Mars) orbiting at 0.029 au from the star. The orbital eccentricity is well constrained and compatible with a circular orbit.

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Bayes Factors

Robert E. Kass, Adrian E. Raftery (1995)

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Gaussian Processes for Machine Learning

Carl Edward Rasmussen, Christopher K. I. Williams (2005)

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.