相关论文: Constraining clumpy dusty torus models using optim…
In critical decision support systems based on medical imaging, the reliability of AI-assisted decision-making is as relevant as predictive accuracy. Although deep learning models have demonstrated significant accuracy, they frequently…
Due to their conjugate posteriors, Gaussian process priors are attractive for estimating the drift of stochastic differential equations with continuous time observations. However, their performance strongly depends on the choice of the…
Dust torus plays the key role in determining active galactic nuclei (AGN) observational appearance. Here, the scenario of accretion regulated central dust torus is tested for the first time in the individual changing-look AGN (CLAGN) SDSS…
In this paper, we use the Bayesian inversion approach to study the data assimilation problem for a family of tumor growth models described by porous-medium type equations. The models contain uncertain parameters and are indexed by a…
Fitting model spectral energy distributions (SED) to galaxy photometric data is a widely used method to recover galaxy parameters from galaxy surveys. However, the parameter space used to describe galaxies is wide and interdependent, and…
We use photometric and spectroscopic infrared observations obtained with the Spitzer Space Telescope of 12 radio-loud active galactic nuclei (AGN) to investigate the dust geometry. Our approach is to look at the change of the infrared…
We present a re-analysis of cosmic shear and galaxy clustering from first-year Dark Energy Survey data (DES Y1), making use of a Hybrid Effective Field Theory (HEFT) approach to model the galaxy-matter relation on weakly non-linear scales,…
Near- and mid-infrared interferometers have resolved the dusty parsec-scale obscurer (torus) around nearby active galactic nuclei (AGNs). With the arrival of extremely large single-aperture telescopes, the emission morphology will soon be…
Many functions have approximately-known upper and/or lower bounds, potentially aiding the modeling of such functions. In this paper, we introduce Gaussian process models for functions where such bounds are (approximately) known. More…
Many clustering algorithms when the data are curves or functions have been recently proposed. However, the presence of contamination in the sample of curves can influence the performance of most of them. In this work we propose a robust,…
In this work we explore the power of future large-scale surveys to constrain possible deviations from the standard single-field slow-roll inflationary scenario. Specifically, we parametrize possible fluctuations around the almost…
We report on the possibility of studying the proprieties of cosmic diffuse baryons by studying self-gravitating clumps and filaments connected to galaxy clusters. While filaments are challenging to detect with X-ray observations, the higher…
We use photometric data, from the optical to the mm for a large sample of optically selected radio-quiet quasars, at low and high redshifts, to test emission models from circum-nuclear dusty torii around them. Model parameters, such as dust…
In this work, geometry optimization of mechanical truss using computer-aided finite element analysis is presented. The shape of the truss is a dominant factor in determining the capacity of load it can bear. At a given parameter space, our…
The optimization of expensive black-box simulators arises in a myriad of modern scientific and engineering applications. Bayesian optimization provides an appealing solution, by leveraging a fitted surrogate model to guide the selection of…
One of the fundamental problems in network analysis is detecting community structure in multi-layer networks, of which each layer represents one type of edge information among the nodes. We propose integrative spectral clustering approaches…
We employ Bayesian Model Averaging (BMA) as a powerful statistical framework to address key cosmological questions about the universe's fundamental properties. We explore extensions beyond the standard $\Lambda$CDM model, considering a…
The process of dynamic state estimation (filtering) based on point process observations is in general intractable. Numerical sampling techniques are often practically useful, but lead to limited conceptual insight about optimal…
We quantify the accuracy with which the cosmological parameters characterizing the energy density of matter (\Omega_m), the amplitude of the power spectrum of matter fluctuations (\sigma_8), the energy density of neutrinos (\Omega_{\nu})…
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,…