相关论文: Constraining clumpy dusty torus models using optim…
We introduce a statistical measure of the effective model complexity, called the Bayesian complexity. We demonstrate that the Bayesian complexity can be used to assess how many effective parameters a set of data can support and that it is a…
The search for physics beyond the Standard Model (BSM) is one of the most important goals for the general purpose detector ATLAS at the Large Hadron Collider at CERN. Already with early LHC data, the ATLAS experiment should be sensitive to…
One of the main unsolved problems of cosmology is how to maximize the extraction of information from nonlinear data. If the data are nonlinear the usual approach is to employ a sequence of statistics (N-point statistics, counting statistics…
We combine near-to-mid-IR Spitzer data with shorter wavelength observations (optical to X-rays) to get insights on the properties of a sample of luminous, obscured Active Galactic Nuclei (AGN). We aim at modeling their broad-band Spectral…
Entropy Search (ES) and Predictive Entropy Search (PES) are popular and empirically successful Bayesian Optimization techniques. Both rely on a compelling information-theoretic motivation, and maximize the information gained about the…
The clustering of local extrema will be exploited to examine Gaussianity, asymmetry, and the footprint of the cosmic-string network on the CMB observed by Planck. The number density of local extrema ($n_{\rm pk}$ for peak and $n_{\rm tr}$…
The increasing richness of data related to cold dense matter, from laboratory experiments to neutron-star observations, requires a framework for constraining the properties of such matter that makes use of all relevant information. Here, we…
The energy spectra of primary and secondary cosmic rays (CR) generally harden at several hundreds of GeV, which can be naturally interpreted by propagation effects. We adopt a spatially dependent CR propagation model to fit the spectral…
Data assimilation algorithms integrate prior information from numerical model simulations with observed data. Ensemble-based filters, regarded as state-of-the-art, are widely employed for large-scale estimation tasks in disciplines such as…
Machine Learning algorithms, such as Boosted Decisions Trees and Deep Neural Network, are widely used in High-Energy-Physics. The aim of this study is to apply Bayesian Optimization to tune the hyperparameters used in a machine learning…
An integral part of the Unified Model for Active Galactic Nuclei (AGNs) is an axisymmetric obscuring medium, which is commonly depicted as a torus of gas and dust surrounding the central engine. However, a robust, dynamical model of the…
This paper describes a new approach to the optimization of information extraction in multi-wavelength image cubes of cosmological fields. The objective is to create a framework for the automatic identification and tagging of sources…
In this work, we present a new perspective on the origin and interpretation of adaptive filters. By applying Bayesian principles of recursive inference from the state-space model and using a series of simplifications regarding the structure…
Cosmological probes pose an inverse problem where the measurement result is obtained through observations, and the objective is to infer values of model parameters which characterize the underlying physical system -- our Universe. Modern…
Bayesian optimization (BO) methods based on information theory have obtained state-of-the-art results in several tasks. These techniques heavily rely on the Kullback-Leibler (KL) divergence to compute the acquisition function. In this work,…
(abridged) We present a model to predict the clustering properties of X-ray clusters in flux-limited surveys. Our technique correctly accounts for past light-cone effects on the observed clustering and follows the non-linear evolution in…
(abridged) We present a new determination of the local temperature function of X-ray clusters. We use a new sample comprising fifty clusters for which temperature information is now available, making it the largest complete sample of its…
Unified schemes of active galactic nuclei (AGN) require an obscuring dusty torus around the central source, giving rise to type 1 line spectrum for pole-on viewing and type 2 characteristics in edge-on sources. Infrared radiation at its…
We present initial results on the use of Mixture Models for density estimation in large astronomical databases. We provide herein both the theoretical and experimental background for using a mixture model of Gaussians based on the…
(abridged) The dust content of the universe is primarily explored via its interaction with stellar photons, producing interstellar extinction. However, owing to the physical extension of the observing beam, observations may detect scattered…