Related papers: Bayesian Hamiltonian Selection in X-ray Photoelect…
How should we gather information to make effective decisions? We address Bayesian active learning and experimental design problems, where we sequentially select tests to reduce uncertainty about a set of hypotheses. Instead of minimizing…
Using Bayesian experimental design techniques, we have shown that for a single two-level quantum mechanical system under strong (projective) measurement, the dynamical parameters of a model Hamiltonian can be estimated with exponentially…
Spectral deconvolution is essential for extracting peak structures that encode material properties and chemical structures, but conventional automated methods often fail when spectra contain high-intensity noise or unknown background…
We apply the Bayesian model selection method (based on the Bayes factor) to optimize $\sqrt{s_\mathrm{NN}}$-dependence in the phenomenological parameters of the (3+1)-dimensional hybrid framework for describing relativistic heavy-ion…
The photon energy spectrum in inclusive weak radiative B -> X_s gamma decay is computed to order alpha_s^2 beta_0. This result is used to extract a value for the HQET parameter $\bar\Lambda$ from the average <1-2E_\gamma/m_B>, and a value…
We present a variation of a quantum algorithm for the machine learning task of classification with graph-structured data. The algorithm implements a feature extraction strategy that is based on Gaussian boson sampling (GBS) a near term…
We delineate an ab initio approach for obtaining x-ray absorption spectra as provided by many-body perturbation theory, together with its realization within an all-electron framework. Employing the Bethe-Salpeter equation, we address…
Real time, singleshot multispectral imaging systems are crucial for environment monitoring and biomedical imaging. Most singleshot multispectral imagers rely on complex computational backends, which precludes real time operations. In this…
In this paper we propose a bayesian approach for near-duplicate image detection, and investigate how different probabilistic models affect the performance obtained. The task of identifying an image whose metadata are missing is often…
Advanced geometrical nanometrology is critical for process control in semiconductor manufacturing, supporting applications in, e.g., photonic integrated circuits, nanoelectronics, and emerging quantum and optoelectronic technologies.…
Purpose: To develop schemes that deliver faithful 2D slices near field heterogeneities of the kind arising from non-ferromagnetic metal implants, with reduced artifacts and shorter scan times. Methods: An excitation scheme relying on…
Hyperspectral images can be represented either as a set of images or as a set of spectra. Spectral classification and segmentation and data reduction are the main problems in hyperspectral image analysis. In this paper we propose a Bayesian…
We describe an automated method for assigning the most likely physical parameters to the components of an eclipsing binary (EB), using only its photometric light curve and combined color. In traditional methods (e.g. WD and EBOP) one…
In this paper we present the experimental protocol and data processing framework for Megahertz X-ray Photon Correlation Spectroscopy (MHz-XPCS) experiments on soft matter samples, implemented at the Materials Imaging and Dynamics (MID)…
Target detection in hyperspectral imagery is the process of locating pixels from an image which are likely to contain target, typically done by comparing one or more spectra for the desired target material to each pixel in the image. Target…
Accurate measurements of photonuclear reaction cross sections are crucial for a number of applications, including radiation shielding design, absorbed dose calculations, reactor physics and engineering, nuclear safeguard and inspection,…
In this study, we demonstrate a sequential experimental design for spectral measurements by active learning using parametric models as predictors. In spectral measurements, it is necessary to reduce the measurement time because of sample…
A new method of extracting the low-lying energy spectrum from Monte Carlo estimates of Euclidean-space correlation functions which incorporates Bayesian inference is described and tested. The procedure fully exploits the information present…
This paper presents a new Bayesian model and algorithm for nonlinear unmixing of hyperspectral images. The model proposed represents the pixel reflectances as linear combinations of the endmembers, corrupted by nonlinear (with respect to…
Bayesian model updating based on Gaussian Process (GP) models has received attention in recent years, which incorporates kernel-based GPs to provide enhanced fidelity response predictions. Although most kernel functions provide high fitting…