相关论文: A Gaussian process framework for modelling stellar…

Context. In order to overcome the radial velocity (RV) precision barrier imposed by stellar variability, there has been a surge of software aimed at simulating and modeling these activity patterns. Aims. We present Analyzing Radial Velocity…

The technique of radial velocity (RV) has produced spectacular discoveries of short-period Jovian mass objects around a fraction (5 to 10%) of nearby G stars. Although we expect Jovian planets to be located in long-period orbits of decades…

We apply Gaussian processes (GP) in order to impose constraints on teleparallel gravity and its $f(T)$ extensions. We use available $H(z)$ observations from (i) cosmic chronometers data (CC); (ii) Supernova Type Ia (SN) data from the…

The study of multiple extrasolar planetary systems has the opportunity to obtain constraints for the planetary masses and orbital inclinations via the detection of mutual perturbations. The analysis of precise radial velocity measurements…

We introduce a new scalable approximation for Gaussian processes with provable guarantees which hold simultaneously over its entire parameter space. Our approximation is obtained from an improved sample complexity analysis for sparse…

We present techniques for effective Gaussian process (GP) modelling of multiple short time series. These problems are common when applying GP models independently to each gene in a gene expression time series data set. Such sets typically…

Aims. We introduce two methods to identify false-positive planetary signals in the context of radial-velocity exoplanet searches. The first is the bi-Gaussian cross-correlation function fitting, and the second is the measurement of…

Owing to recent advances in radial-velocity instrumentation and observation techniques, the detection of Earth-mass planets around Sun-like stars may soon be primarily limited by intrinsic stellar variability. Several processes contribute…

The Gaia mission is a magnitude-limited whole-sky survey that collects an impressive quantity of astrometric, spectro-photometric and spectroscopic data. Among all the on-board instruments, the Radial Velocity Spectrometer (RVS) produces…

Gaussian processes (GPs) are widely used as surrogate models for complicated functions in scientific and engineering applications. In many cases, prior knowledge about the function to be approximated, such as monotonicity, is available and…

Despite their promise and ubiquity, Gaussian processes (GPs) can be difficult to use in practice due to the computational impediments of fitting and sampling from them. Here we discuss a short R package for efficient multivariate normal…

We present a novel computational approach for extracting weak signals, whose exact location and width may be unknown, from complex background distributions with an arbitrary functional form. We focus on datasets that can be naturally…

In spite of the diverse literature on nonstationary spatial modeling and approximate Gaussian process (GP) methods, there are no general approaches for conducting fully Bayesian inference for moderately sized nonstationary spatial data sets…

Radial velocity surveys for extra-solar planets generally require substantial amounts of large telescope time in order to monitor a sufficient number of stars. Two of the aspects which can limit such surveys are the single-object…

Computer experiments involving both qualitative and quantitative (QQ) factors have attracted increasing attention. Gaussian process (GP) models have proven effective in this context by choosing specialized covariance functions for QQ…

Using the far-UV (FUV) and near-UV (NUV) photometry from the NASA Galaxy Evolution Explorer (GALEX), we searched for evidence of increased stellar activity due to tidal and/or magnetic star-planet interactions (SPI) in the 272 of known FGK…

Context. Distinguishing between a signal induced by either stellar activity or a planet is currently the main challenge in radial velocity searches for low-mass exoplanets. Even when the presence of a transiting planet and hence its period…

A multi-task Gaussian process (GP) machine learning model is introduced to simultaneously predict two important nuclear observables across the nuclear chart, namely nuclear masses and charge radii. Utilizing 12 physical input features, our…

Gaussian stochastic process emulation is a powerful tool for approximating computationally intensive computer models. However, estimation of parameters in the GaSP emulator is a challenging task. No closed-form estimator is available, and…

Precise measurements of a star's radial velocity (RV) made using extremely stable, high resolution, optical or near infrared spectrographs can be used to determine the masses and orbital parameters of gravitationally-bound extra-solar…