相关论文: Bayesian peak-bagging of solar-like oscillators us…
A practical guide is presented to some of the main data analysis concepts and techniques employed contemporarily in the asteroseismic study of stars exhibiting solar-like oscillations. The subjects of digital signal processing and spectral…
New insights on stellar evolution and stellar interiors physics are being made possible by asteroseismology. Throughout the course of the Kepler mission, asteroseismology has also played an important role in the characterization of…
Helioseismology has enabled us to better understand the solar interior, while also allowing us to better constrain solar models. But now is a tremendous epoch for asteroseismology as space missions dedicated to studying stellar oscillations…
Multi-parameter Bayesian inferences based on Markov chain Monte Carlo (MCMC) samples have been widely used to estimate red noise in solar period-periodic signals. To MCMC, proper priors and sufficient iterations are prerequisites ensuring…
Rich data generating mechanisms are ubiquitous in this age of information and require complex statistical models to draw meaningful inference. While Bayesian analysis has seen enormous development in the last 30 years, benefitting from the…
This study develops a robust framework for exoplanet characterization by leveraging asteroseismic constraints on host stars. Using precise photometric data from missions such as \textit{Kepler} and \textit{TESS}, we derive stellar…
This paper introduces a framework for speeding up Bayesian inference conducted in presence of large datasets. We design a Markov chain whose transition kernel uses an (unknown) fraction of (fixed size) of the available data that is randomly…
We present the Bayesian Asteroseismology data Modeling (BAM) pipeline, an automated asteroseismology pipeline that returns global oscillation parameters and granulation parameters from the analysis of photometric time-series. BAM also…
There is increasing interest to develop Bayesian inferential algorithms for point process models with intractable likelihoods. A purpose of this paper is to illustrate the utility of using simulation based strategies, including Approximate…
The TESS mission has provided a wealth of asteroseismic data for solar-like oscillators. However, these data are subject to varying cadences, large gaps, and unequal sampling, which complicates analysis in the frequency domain. One solution…
We present a comprehensive comparison of different Markov Chain Monte Carlo (MCMC) sampling methods, evaluating their performance on both standard test problems and cosmological parameter estimation. Our analysis includes traditional…
The long-term monitoring and high photometric precision of the Kepler satellite will provide a unique opportunity to sound the stellar cycles of many solar-type stars using asteroseismology. This can be achieved by studying periodic changes…
We describe a Bayesian approach to estimating luminosity functions. We derive the likelihood function and posterior probability distribution for the luminosity function, given the observed data, and we compare the Bayesian approach with…
Asteroseismology is an exceptional tool for studying stars by using the properties of observed modes of oscillation. So far the process of performing an asteroseismic analysis of a star has remained somewhat esoteric and inaccessible to…
Asteroseismology of solar-type stars has entered a new era of large surveys with the success of the NASA \textit{Kepler} mission, which is providing exquisite data on oscillations of stars across the Hertzprung-Russell (HR) diagram. From…
The pattern of individual mode frequencies in solar-like oscillators provides valuable insight into their properties and interior structures. The identification and characterisation of these modes requires high signal-to-noise and frequency…
Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo (MCMC) sampling methods are used to…
Recent developments in big data and analytics research have produced an abundance of large data sets that are too big to be analyzed in their entirety, due to limits on computer memory or storage capacity. To address these issues,…
This paper introduces a Bayesian framework that combines Markov chain Monte Carlo (MCMC) sampling, dimensionality reduction, and neural density estimation to efficiently handle inverse problems that (i) must be solved multiple times, and…
In this paper, we present a method for computing the marginal likelihood, also known as the model likelihood or Bayesian evidence, from Markov Chain Monte Carlo (MCMC), or other sampled posterior distributions. In order to do this, one…