Related papers: Reconstructing AGN X-ray spectral parameter distri…

While Bayesian inference provides a principled framework for reasoning under uncertainty, its widespread adoption is limited by the intractability of exact posterior computation, necessitating the use of approximate inference. However,…

X-ray spectroscopy of active galactic nuclei (AGN) reveals key information about circumnuclear geometry. Many AGN show a narrow Fe K-alpha line at 6.4 keV and associated Compton-scattered continua, produced by primary continuum scattering…

We present a new inference method based on approximate Bayesian computation for estimating parameters governing an entire network based on link-traced samples of that network. To do this, we first take summary statistics from an observed…

Aims. We present an innovative artificial neural network (ANN) architecture, called Generative ANN (GANN), that computes the forward model, that is it learns the function that relates the unknown outputs (stellar atmospheric parameters, in…

The need to analyze the available large synoptic multi-band surveys drives the development of new data-analysis methods. Photometric redshift estimation is one field of application where such new methods improved the results, substantially.…

Approximate Bayesian computation (ABC) methods, which are applicable when the likelihood is difficult or impossible to calculate, are an active topic of current research. Most current ABC algorithms directly approximate the posterior…

Finding and characterizing the population of active galactic nuclei (AGNs) that produces the X-ray background (XRB) is necessary to connect the history of accretion to observations of galaxy evolution at longer wavelengths. The year 2012…

Data assimilation leads naturally to a Bayesian formulation in which the posterior probability distribution of the system state, given the observations, plays a central conceptual role. The aim of this paper is to use this Bayesian…

We propose a method to restore and to segment simultaneously images degraded by a known point spread function (PSF) and additive white noise. For this purpose, we propose a joint Bayesian estimation framework, where a family of…

The active galactic nuclei X-ray luminosity function traces actively accreting supermassive black holes and is essential for the study of the properties of the active galactic nuclei (AGN) population, black hole evolution, and galaxy-black…

Machine learning models are commonly trained end-to-end and in a supervised setting, using paired (input, output) data. Examples include recent super-resolution methods that train on pairs of (low-resolution, high-resolution) images.…

X-rays offer a reliable method to identify Active Galactic Nuclei (AGN). However, in the high-redshift Universe X-ray AGN are poorly sampled due to their relatively low space density and the small areas covered by X-ray surveys. In addition…

We combine multiwavelength data in the AEGIS-XD and C-COSMOS surveys to measure the typical dark matter halo mass of X-ray selected AGN [Lx(2-10keV)>1e42 erg/s] in comparison with far-infrared selected star-forming galaxies detected in the…

We perform spectral energy distribution (SED) fitting to 711 luminous X-ray AGN at 0.7 < z < 4.5 using 10-bands of optical and infra-red photometric data for objects within XMM-SERVS. This fitting provided 510 reliable (reduced $\chi ^2 <…

Galaxy photometric redshift (photo-$z$) is crucial in cosmological studies, such as weak gravitational lensing and galaxy angular clustering measurements. In this work, we try to extract photo-$z$ information and construct its probability…

Multi-wavelength surveys covering large sky volumes are necessary to obtain an accurate census of rare objects such as high luminosity and/or high redshift active galactic nuclei (AGN). Stripe 82X is a 31.3 deg$^2$ X-ray survey with…

Neural networks are popular state-of-the-art models for many different tasks.They are often trained via back-propagation to find a value of the weights that correctly predicts the observed data. Although back-propagation has shown good…

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…

A theoretically interesting and practically important question in cosmology is the reconstruction of the initial density distribution provided a late-time density field. This is a long-standing question with a revived interest recently,…

We present a method for the identification of heavily absorbed AGN (NH>10^23 cm^-2) from X-ray photometric data. We do this using a set of XMM-Newton reference spectra of local galaxies for which we have accurate NH information, as…