Related papers: Reconstructing AGN X-ray spectral parameter distri…
We present the spectral energy distributions (SEDs) of a hard X-ray selected sample. The sample contains 136 sources with F(2-10 keV)>10^-14 erg/cm^2/s and 132 are AGNs. The sources are detected in a 1 square degree area of the…
Approximate Bayesian computation (ABC) is a likelihood-free approach for Bayesian inferences based on a rejection algorithm method that applies a tolerance of dissimilarity between summary statistics from observed and simulated data.…
We introduce the Hessian reweighting of parton distribution functions (PDFs). Similarly to the better-known Bayesian methods, its purpose is to address the compatibility of new data and the quantitative modifications they induce within an…
We present a Bayesian reconstruction method which maps a galaxy distribution from redshift-space to real-space inferring the distances of the individual galaxies. The method is based on sampling density fields assuming a lognormal prior…
Deep generative models are rapidly gaining traction in medical imaging. Nonetheless, most generative architectures struggle to capture the underlying probability distributions of volumetric data, exhibit convergence problems, and offer no…
Bayesian learning provides a unified skeleton to solve the electrophysiological source imaging task. From this perspective, existing source imaging algorithms utilize the Gaussian assumption for the observation noise to build the likelihood…
We present a neural network classification (NNC) method for photometric redshift estimation that produces well-calibrated redshift probability density functions (PDFs). The method discretizes the redshift space into ordered bins and…
This paper introduces a stochastic plug-and-play (PnP) sampling algorithm that leverages variable splitting to efficiently sample from a posterior distribution. The algorithm based on split Gibbs sampling (SGS) draws inspiration from the…
We have constrained the extragalactic source count distributions over a broad range of X-ray fluxes and in various energy bands to test whether the predictions from X-ray background synthesis models agree with the observational constraints…
Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts while accurately quantifying associated uncertainties. In this…
We estimate the incidence of multiply-imaged AGNs among the optical counterparts of X-ray selected point-like sources in the XXL field. We also derive the expected statistical properties of this sample, such as the redshift distribution of…
We use Bayesian inference and nested sampling to develop a non-parametric method to reconstruct the primordial power spectrum $P_{\mathcal{R}}(k)$ from Large Scale Structure (LSS) data. The performance of the method is studied by applying…
We propose a universal background subtraction framework based on the Arithmetic Distribution Neural Network (ADNN) for learning the distributions of temporal pixels. In our ADNN model, the arithmetic distribution operations are utilized to…
X-ray spectroscopy of heavily obscured Active Galactic Nuclei (AGN) offers a unique opportunity to study the circum-nuclear environment of accreting supermassive black holes (SMBHs). However, individual models describing the obscurer have…
Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques, such as Markov chain Monte Carlo (MCMC) and particle filters, have become very popular in signal processing over the last years. However, in many…
Our aim in this work is to answer, using simulated narrow-band photometry data, the following general question: What can we learn about galaxies from these new generation cosmological surveys? For instance, can we estimate stellar age and…
The X-ray background (XRB) is due to the aggregate of active galactic nuclei (AGN), which peak in activity at z~1 and is often modeled as the sum of different proportions of unabsorbed, moderately- and heavily-absorbed AGN. We present the…
Many modern statistical applications involve inference for complicated stochastic models for which the likelihood function is difficult or even impossible to calculate, and hence conventional likelihood-based inferential echniques cannot be…
The majority of active galactic nuclei (AGN) are obscured by large amounts of absorbing material that makes them invisible at many wavelengths. X-rays, given their penetrating power, provide the most secure way for finding these AGN. The…
Variational methods are attractive for computing Bayesian inference for highly parametrized models and large datasets where exact inference is impractical. They approximate a target distribution - either the posterior or an augmented…