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

Approximate Bayesian computation (ABC) methods are used to approximate posterior distributions using simulation rather than likelihood calculations. We introduce Gaussian process (GP) accelerated ABC, which we show can significantly reduce…

Bayesian inference is often used in cosmology and astrophysics to derive constraints on model parameters from observations. This approach relies on the ability to compute the likelihood of the data given a choice of model parameters. In…

We present a novel method for image anomaly detection, where algorithms that use samples drawn from some distribution of "normal" data, aim to detect out-of-distribution (abnormal) samples. Our approach includes a combination of encoder and…

The spectroscopic analysis of 117 serendipitous sources in the HELLAS2XMM 1df (1 degree field) survey is described. Of these, 106 sources, of which 86% have a spectroscopic redshift, are used to evaluate the fraction of X-ray absorbed (log…

We present an analysis of a sample of 69 local obscured Swift/Burst Alert Telescope active galactic nuclei (AGNs) with X-ray spectra from NuSTAR and infrared (IR) spectral energy distributions from Herschel and WISE. We combine this X-ray…

Photon counts statistics have recently been proven to provide a sensitive observable for characterizing gamma-ray source populations and for measuring the composition of the gamma-ray sky. In this work, we generalize the use of the standard…

We developed a Deep Convolutional Neural Network (CNN), used as a classifier, to estimate photometric redshifts and associated probability distribution functions (PDF) for galaxies in the Main Galaxy Sample of the Sloan Digital Sky Survey…

We aim to determine the most effective approach for estimating uncertainties in quasar photo-$z$ and to evaluate the ability of different models to reconstruct the true redshift distribution under varying data quality. We use photometric…

We report the multi-wavelength identification of the X-ray sources found in the Subaru-XMM-Newton Deep Survey (SXDS) using deep imaging data covering the wavelength range between the far-UV to the mid-IR. We select a primary counterpart of…

Empirical "backward" galaxy evolution models for infrared bright galaxies are constrained using multi-band infrared surveys. We developed a new Monte-Carlo algorithm for this task, implementing luminosity dependent distribution functions…

In Bayesian inverse problems sampling the posterior distribution is often a challenging task when the underlying models are computationally intensive. To this end, surrogates or reduced models are often used to accelerate the computation.…

Approximate Bayesian Computation (ABC) is a useful class of methods for Bayesian inference when the likelihood function is computationally intractable. In practice, the basic ABC algorithm may be inefficient in the presence of discrepancy…

Inverse problems are ubiquitous in nature, arising in almost all areas of science and engineering ranging from geophysics and climate science to astrophysics and biomechanics. One of the central challenges in solving inverse problems is…

In computed tomography, data consist of measurements of the attenuation of X-rays passing through an object. The goal is to reconstruct the linear attenuation coefficient of the object's interior. For each position of the X-ray source,…

In modeling the X-ray spectra of active galactic nuclei (AGNs), the inclination angle is a parameter that can play an important role in analyzing the X-ray spectra of AGN, but it has never been studied in detail. We present a broadband…

Approximate Bayesian Computation (ABC) is a method to obtain a posterior distribution without a likelihood function, using simulations and a set of distance metrics. For that reason, it has recently been gaining popularity as an analysis…

Approximate Bayesian Computation (ABC) is a popular inference method when likelihoods are hard to come by. Practical bottlenecks of ABC applications include selecting statistics that summarize the data without losing too much information or…

We present a Bayesian reconstruction algorithm that infers the three-dimensional large-scale matter distribution from the weak gravitational lensing effects measured in the image shapes of galaxies. The algorithm is designed to also work…

We present population synthesis modeling of the X-ray background with genetic algorithm - based optimization method. In our models the best fit could be achieved for lower values of high-energy exponential cut-off (~ 170 keV) and larger…

An empirical forward-modeling framework is developed to interpret the multiwavelength properties of Active Galactic Nuclei (AGN) and provide insights into the overlap and incompleteness of samples selected at different parts of the…