DIAMONDS: A new Bayesian nested sampling tool

Abstract

In the context of high-quality asteroseismic data provided by the NASA Kepler mission, we developed a new code, termed Diamonds (high-DImensional And multi-MOdal NesteD Sampling), for fast Bayesian parameter estimation and model comparison by means of the Nested Sampling Monte Carlo (NSMC) algorithm, an efficient and powerful method very suitable for high-dimensional problems (like the peak bagging analysis of solar-like oscillations) and multi-modal problems (i.e. problems that show multiple solutions). We applied the code to the peak bagging analysis of solar-like oscillations observed in a challenging F-type star. By means of Diamonds one is able to detect the different backgrounds in the power spectrum of the star (e.g. stellar granulation and faculae activity) and to understand whether one or two oscillation peaks can be identified or not. In addition, we demonstrate a novel approach to peak bagging based on multimodality, which is able to reduce significantly the number of free parameters involved in the peak bagging model. This novel approach is therefore of great interest for possible future automatization of the entire analysis technique.

Conference Proceeding Poster - CoRoT3-KASC7 The Space Photometry Revolution, Toulouse, France, July 6-11 2014, 2 pages, 3 figures

References in corpus (4)

• Preparation of Kepler lightcurves for asteroseismic analyses
• Efficient Bayesian inference for multimodal problems in cosmology
• DIAMONDS: a new Bayesian Nested Sampling tool. Application to Peak Bagging of solar-like oscillations
• Bayesian peak-bagging of solar-like oscillators using MCMC: A comprehensive guide

Cited by in corpus (2)

• Nested sampling for physical scientists
• Searching for solar-like oscillations in pre-main sequence stars using APOLLO