相关论文: Sparse Bayesian Imaging of Solar Flares

In this article, an overview of Bayesian methods for sequential simulation from posterior distributions of nonlinear and non-Gaussian dynamic systems is presented. The focus is mainly laid on sequential Monte Carlo methods, which are based…

AI-FLARES (Artificial Intelligence for the Analysis of Solar Flares Data) is a research project funded by the Agenzia Spaziale Italiana and by the Istituto Nazionale di Astrofisica within the framework of the ``Attivit\`a di Studio per la…

Flares close to the solar limb, where the footpoints are occulted, can reveal the spectrum and structure of the coronal loop-top source in X-rays. We aim at studying the properties of the corresponding energetic electrons near their…

This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to…

This paper addresses the problem of identifying a lower dimensional space where observed data can be sparsely represented. This under-complete dictionary learning task can be formulated as a blind separation problem of sparse sources…

The model of stochastic acceleration of particles by turbulence has been successful in explaining many observed features of solar flares. Here we demonstrate a new method to obtain the accelerated electron spectrum and important…

The deduction from solar flare X-ray photon spectroscopic data of the energy dependent model-independent spectral index is considered as an inverse problem. Using the well developed regularization approach we analyze the energy dependency…

The RHESSI experiment uses rotational modulation for x- and gamma ray imaging of solar eruptions. In order to disentangle rotational modulation from intrinsic time variation, an unbiased linear estimator for the spatially integrated photon…

Astronomers are often confronted with funky populations and distributions of objects: brighter objects are more likely to be detected; targets are selected based on colour cuts; imperfect classification yields impure samples. Failing to…

We compare four different methods of calculating uncertainty estimates in fitting parameterized models to RHESSI X-ray spectra, considering only statistical sources of error. Three of the four methods are based on estimating the scale-size…

To solve a number of problems in solar physics related to mechanisms of energy release in solar corona parameters of hot coronal plasma are required, such as energy distribution, emission measure, differential emission measure, and their…

Over the past 30 years, numerous large-scale photometric astronomical surveys have been conducted, including SDSS, Pan-STARRS, Gaia,2MASS, WISE, and others. These surveys provide extensive photometric measurements that can be used to infer…

The Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) has observed more than 80,000 solar energetic events since its launch on February $12^{\rm th}$, 2002. Using this large sample of observed flares, we studied the…

A Bayesian probability based approach is applied to the problem of detecting and parameterizing oscillations in the upper solar atmosphere for the first time. Due to its statistical origin, this method provides a mechanism for determining…

We propose a Bayesian inference framework to estimate uncertainties in inverse scattering problems. Given the observed data, the forward model and their uncertainties, we find the posterior distribution over a finite parameter field…

We propose a novel framework for joint magnetic resonance image reconstruction and uncertainty quantification using under-sampled k-space measurements. The problem is formulated as a Bayesian linear inverse problem, where prior…

Deconvolution of astronomical images is a key aspect of recovering the intrinsic properties of celestial objects, especially when considering ground-based observations. This paper explores the use of diffusion models (DMs) and the Diffusion…

We present a Bayesian hierarchical framework to analyze photometric galaxy survey data with stellar population synthesis (SPS) models. Our method couples robust modeling of spectral energy distributions with a population model and a noise…

Data analysis methods have always been of critical importance for quantitative sciences. In astronomy, the increasing scale of current and future surveys is driving a trend towards a separation of the processes of low-level data reduction…

Spatially resolved observations of the Sun and the astronomical sample size of stellar bodies are the respective key strengths of solar and stellar observations. However, the large difference in object brightness between the Sun and other…