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Register a new userA Bayesian approach to the modelling of alpha Cen A
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Determining the physical characteristics of a star is an inverse problem consisting in estimating the parameters of models for the stellar structure and evolution, knowing certain observable quantities. We use a Bayesian approach to solve this problem for alpha Cen A, which allows us to incorporate prior information on the parameters to be estimated, in order to better constrain the problem. Our strategy is based on the use of a Markov Chain Monte Carlo (MCMC) algorithm to estimate the posterior probability densities of the stellar parameters: mass, age, initial chemical composition,... We use the stellar evolutionary code ASTEC to model the star. To constrain this model both seismic and non-seismic observations were considered. Several different strategies were tested to fit these values, either using two or five free parameters in ASTEC. We are thus able to show evidence that MCMC methods become efficient with respect to more classical grid-based strategies when the number of parameters increases. The results of our MCMC algorithm allow us to derive estimates for the stellar parameters and robust uncertainties thanks to the statistical analysis of the posterior probability densities. We are also able to compute odds for the presence of a convective core in alpha Cen A. When using core-sensitive seismic observational constraints, these can raise above ~40%. The comparison of results to previous studies also indicates that these seismic constraints are of critical importance for our knowledge of the structure of this star.
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We infer from different seismic observations the energy supplied per unit of time by turbulent convection to the acoustic modes of Alpha Cen A (HD 128620), a star which is similar but not identical to the Sun. The inferred rates of energy supplied to the modes (i.e. mode excitation rates) are found to be significantly larger than in the Sun. They are compared with those computed with an excitation model that includes two sources of driving, the Reynolds stress contribution and the advection of entropy fluctuations. The model also uses a closure model, the Closure Model with Plumes (CMP hereafter), that takes the asymmetry between the up- and down-flows (i.e. the granules and plumes, respectively) into account. Different prescriptions for the eddy-time correlation function are also confronted to observational data. Calculations based on a Gaussian eddy-time correlation underestimate excitation rates compared with the values derived from observations for Alpha Cen A. On the other hand, calculations based on a Lorentzian eddy-time correlation lie within the observational error bars. This confirms results obtained in the solar case. With respect to the helioseismic data, those obtained for Alpha Cen A constitute an additional support for our model of excitation. We show that mode masses must be computed taking turbulent pressure into account. Finally, we emphasize the need for more accurate seismic measurements in order to discriminate, in the case of Alpha Cen A, between the CMP closure model and the quasi-Normal Approximation as well as to confirm or not the need to include the excitation by the entropy fluctuations.
A set of long and nearly continuous observations of alpha Centauri A should allow us to derive an accurate set of asteroseismic constraints to compare to models, and make inferences on the internal structure of our closest stellar neighbour. We intend to improve the knowledge of the interior of alpha Centauri A by determining the nature of its core. We combined the radial velocity time series obtained in May 2001 with three spectrographs in Chile and Australia: CORALIE, UVES, and UCLES. The resulting combined time series has a length of 12.45 days and contains over 10,000 data points and allows to greatly reduce the daily alias peaks in the power spectral window. We detected 44 frequencies that are in good overall agreement with previous studies, and found that 14 of these show possible rotational splittings. New values for the large and small separations have been derived. A comparison with stellar models indicates that the asteroseismic constraints determined in this study allows us to set an upper limit to the amount of convective-core overshooting needed to model stars of mass and metallicity similar to those of alpha Cen A.
We present a fully self-consistent, line-by-line differential abundance analysis of $alpha$ Cen AB based on high-quality HARPS data. Various line lists are used and analysis strategies implemented to improve the reliability of the results. Abundances of 21 species with a typical precision of 0.02-0.03 dex are reported. We find that the chemical composition of the two stars is not scaled solar (e.g. Na and Ni excess, depletion of neutron-capture elements), but that their patterns are strikingly similar, with a mean abundance difference (A - B) with respect to hydrogen of -0.01$pm$0.04 dex. Much of the scatter may be ascribed to physical effects that are not fully removed through a differential analysis because of the mismatch in parameters between the two components. We derive an age for the system from abundance indicators (e.g. [Y/Mg] and [Y/Al]) that is slightly larger than solar and in agreement with most asteroseismic results. Assuming coeval formation for the three components belonging to the system, this implies an age of about $sim$6 Gyrs for the M dwarf hosting the terrestrial planet Proxima Cen b. After correction for Galactic chemical evolution effects, we find a trend between the abundance ratios and condensation temperature in $alpha$ Cen A akin to that of the Sun. However, taking this finding as evidence for the sequestration of rocky material locked up in planets may be premature given that a clear link between the two phenomena remains to be established. The similarity between the abundance pattern of the binary components argues against the swallowing of a massive planet by one of the stars after the convective zones have shrunk to their present-day sizes.
Analyzing electronic health records (EHR) poses significant challenges because often few samples are available describing a patients health and, when available, their information content is highly diverse. The problem we consider is how to integrate sparsely sampled longitudinal data, missing measurements informative of the underlying health status and fixed demographic information to produce estimated survival distributions updated through a patients follow up. We propose a nonparametric probabilistic model that generates survival trajectories from an ensemble of Bayesian trees that learns variable interactions over time without specifying beforehand the longitudinal process. We show performance improvements on Primary Biliary Cirrhosis patient data.
A new method is presented for modelling the physical properties of galaxy clusters. Our technique moves away from the traditional approach of assuming specific parameterised functional forms for the variation of physical quantities within the cluster, and instead allows for a free-form reconstruction, but one for which the level of complexity is determined automatically by the observational data and may depend on position within the cluster. This is achieved by representing each independent cluster property as some interpolating or approximating function that is specified by a set of control points, or nodes, for which the number of nodes, together with their positions and amplitudes, are allowed to vary and are inferred in a Bayesian manner from the data. We illustrate our nodal approach in the case of a spherical cluster by modelling the electron pressure profile Pe(r) in analyses both of simulated Sunyaev-Zeldovich (SZ) data from the Arcminute MicroKelvin Imager (AMI) and of real AMI observations of the cluster MACS J0744+3927 in the CLASH sample. We demonstrate that one may indeed determine the complexity supported by the data in the reconstructed Pe(r), and that one may constrain two very important quantities in such an analysis: the cluster total volume integrated Comptonisation parameter (Ytot) and the extent of the gas distribution in the cluster (rmax). The approach is also well-suited to detecting clusters in blind SZ surveys.
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