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

We consider Bayesian nonparametric density estimation using a Pitman-Yor or a normalized inverse-Gaussian process kernel mixture as the prior distribution for a density. The procedure is studied from a frequentist perspective. Using the…

The health condition of components in civil infrastructures can be described by various discrete states according to their performance degradation. Inferring these states from measurable responses is typically an ill-posed inverse problem.…

We have measured the X-ray spectral properties of a complete flux-limited sample of bright AGNs from HEAO-1 all-sky catalogs to investigate their statistics and provide greater constraints on the bright-end of the hard X-ray luminosity…

We consider nonparametric Bayesian estimation inference using a rescaled smooth Gaussian field as a prior for a multidimensional function. The rescaling is achieved using a Gamma variable and the procedure can be viewed as choosing an…

We adopt a Bayesian X-ray spectral approach to investigate the accretion properties of unobscured ($20<\log(N_{\rm H}/{\rm cm}^{-2}<22$) and obscured ($22< \log(N_{\rm H}/{\rm cm}^{-2}<24$) active galactic nuclei (AGN) to shed light on the…

Optical variability has proven to be an effective way of detecting AGNs in imaging surveys, lasting from weeks to years. In the present work we test its use as a tool to identify AGNs in the VST multi-epoch survey of the COSMOS field,…

In today's era, a tremendous amount of data is generated by different observatories and manual classification of data is something which is practically impossible. Hence, to classify and categorize the objects there are multiple machine and…

Approximate Bayesian computation (ABC) is a set of techniques for Bayesian inference when the likelihood is intractable but sampling from the model is possible. This work presents a simple yet effective ABC algorithm based on the…

We study the statistical properties of the Normalized Excess Variance of variability process characterized by a red-noise power spectral density (PSD), as the case of Active Galactic Nuclei (AGN). We perform Monte Carlo simulations of…

Computer vision tasks are difficult because of the large variability in the data that is induced by changes in light, background, partial occlusion as well as the varying pose, texture, and shape of objects. Generative approaches to…

We consider a prior for nonparametric Bayesian estimation which uses finite random series with a random number of terms. The prior is constructed through distributions on the number of basis functions and the associated coefficients. We…

We present a new method to predict the line-of-sight column density (NH) values of active galactic nuclei (AGN) based on mid-infrared (MIR), soft, and hard X-ray data. We developed a multiple linear regression machine learning algorithm…

Weak lensing mass-mapping is a useful tool to access the full distribution of dark matter on the sky, but because of intrinsic galaxy ellipticies and finite fields/missing data, the recovery of dark matter maps constitutes a challenging…

The estimation and utilization of photometric redshift probability density functions (photo-$z$ PDFs) has become increasingly important over the last few years and currently there exist a wide variety of algorithms to compute photo-$z$'s,…

We use $\sim 1,000$ X-ray sources in the COSMOS-Legacy survey and study the position of the AGN relative to the star forming main sequence (MS). We also construct a galaxy (non-AGN) reference sample that includes $\sim 90,000$ sources. We…

We present the first direct measurements of the rest-frame 10-40 keV X-ray luminosity function (XLF) of Active Galactic Nuclei (AGNs) based on a sample of 94 sources at 0.1 < z <3, selected at 8-24 keV energies from sources in the NuSTAR…

One of the consequences of entering the era of precision cosmology is the widespread adoption of photometric redshift probability density functions (PDFs). Both current and future photometric surveys are expected to obtain images of…

We use a combination of the XMM-Newton serendipitous X-ray survey with the optical SDSS, and the infrared WISE all-sky survey in order to check the efficiency of the low X-ray to infrared luminosity selection method in finding heavily…

Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights. Recent work developing this class of methods has explored ever richer parameterizations of the…

Many astrophysical analyses depend on estimates of redshifts (a proxy for distance) determined from photometric (i.e., imaging) data alone. Inaccurate estimates of photometric redshift uncertainties can result in large systematic errors.…