Related papers: Emulator-based Bayesian Inference on Non-Proportio…
This paper, presents a simple method for accurate calibration of organic scintillation detectors. The method is based on the fact that differentiating the response function leads to accurate estimation of the Compton edge. The…
Accurate energy channel calibration in scintillation detectors is essential for reliable radiation detection across nuclear physics, medical imaging, and environmental monitoring. Organic scintillators like BC408 and EJ309 lack full-energy…
Scintillation detectors are essential tools for radiation measurement, but calibrating them accurately can be challenging, especially when full-energy peaks are not prominent. This is common in detectors like plastic scintillators. Current…
We are designing an all-scintillator Compton gamma imager for use in security investigations and remediation actions involving radioactive threat material. To satisfy requirements for a rugged and portable instrument, we have chosen solid…
In this paper we present a method of scintillation detector energy calibration using the gamma-rays. The technique is based on the Compton scattering of gamma-rays in a scintillation detector and subsequent photoelectric absorption of the…
Recent experiments aiming to measure phenomena predicted by strong field quantum electrodynamics have done so by colliding relativistic electron beams and high-power lasers. In such experiments, measurements of the collision parameters are…
The universal tendency in scanning probe microscopy (SPM) over the last two decades is to transition from simple 2D imaging to complex detection and spectroscopic imaging modes. The emergence of complex SPM engines brings forth the…
A light-yield calibration of an NE 213A organic liquid scintillator detector has been performed using both monoenergetic and polyenergetic gamma-ray sources. Scintillation light was detected in a photomultiplier tube, and the corresponding…
This paper presents a statistical forward model for a Compton imaging system, called Compton imager. This system, under development at the University of Illinois Urbana Champaign, is a variant of Compton cameras with a single type of…
In this paper, we study a fast approximate inference method based on expectation propagation for exploring the posterior probability distribution arising from the Bayesian formulation of nonlinear inverse problems. It is capable of…
Bayesian model selection provides a powerful and mathematically transparent framework to tackle hypothesis testing, such as detection tests of gravitational waves emitted during the coalescence of binary systems using ground-based laser…
Scintillation detector has lower energy resolution for Gamma-ray as compared to semiconductor detector, better spectra analysis method is essential to traditional method. A model for describing the response function of scintillation…
Bayesian estimation is a powerful theoretical paradigm for the operation of quantum sensors. However, the Bayesian method for statistical inference generally suffers from demanding calibration requirements that have so far restricted its…
Position sensitive detectors based on gaseous scintillation proportional counters with Anger-type readout are being used in several research areas such as neutron detection, search for dark matter and neutrinoless double beta decay. Design…
Bayesian inference is an effective approach for solving statistical learning problems especially with uncertainty and incompleteness. However, inference efficiencies are physically limited by the bottlenecks of conventional computing…
Numerical integration and emulation are fundamental topics across scientific fields. We propose novel adaptive quadrature schemes based on an active learning procedure. We consider an interpolative approach for building a surrogate…
Constancy and anisotropy of vacuum refractive index serves as a strong way to probe the predictions of theories beyond the standard model (BSM), especially those that predict breaking of local Lorentz and CPT symmetries. For photons of…
Complex simulator-based models are now routinely used to perform inference across the sciences and engineering, but existing inference methods are often unable to account for outliers and other extreme values in data which occur due to…
Simulator-based models are models for which the likelihood is intractable but simulation of synthetic data is possible. They are often used to describe complex real-world phenomena, and as such can often be misspecified in practice.…
We propose a hierarchical Bayesian model and state-of-art Monte Carlo sampling method to solve the unfolding problem, i.e., to estimate the spectrum of an unknown neutron source from the data detected by an organic scintillator. Inferring…