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The beta-decay of $\rm^{85}Kr$ is a significant radioactive background for experiments that use liquified noble gases to search for dark matter and measure the low-energy solar neutrino flux. While there are several proposed methods for…
Research reactors host a wide range of activities that make use of the intense neutron fluxes generated at these facilities. Recent interest in performing measurements with relatively low event rates, e.g. reactor antineutrino detection, at…
Subsurface radioactivity may be due to transport of radionuclides from a contaminated surface into the solid volume, as occurs for radioactive fallout deposited on soil, or from fast neutron activation of a solid volume, as occurs in…
Spectrum sensing is essential in cognitive radio to enable dynamic spectrum access. In many scenarios, primary user signal must be detected reliably in low signal-to-noise ratio (SNR) regime under required sensing time. We propose to use…
Over the last two decades the cold-atom physics has matured from proof-of-principle demonstrations to a versatile platform for precision measurements and study of quantum phenomena. Ultra-cold atomic ensembles have been used both for…
We introduce a method to dispersively detect alkali atoms in radio-frequency dressed states. In particular, we use dressed detection to measure populations and population differences of atoms prepared in their clock states. Linear…
Autonomous hazard detection and avoidance is a key technology for future landing missions in unknown surface conditions. Current state-of-the-art stochastic algorithms assume simple Gaussian measurement noise on dense, high-fidelity digital…
This paper overviews certain radiation detection, perception, and planning challenges for nuclearized robotics that aim to support the waste management and decommissioning mission. To enable the autonomous monitoring, inspection and…
Resonant frequency modulation spectroscopy has been previously used as a highly-sensitive method for measuring the output of cold atom interferometers. Using a detailed model that accounts for optical saturation, laser intensities and…
We present a new technique for observing low energy neutrinos with the aim of detecting the cosmic neutrino background using ion storage rings. Utilising high energy targets exploits the quadratic increase in the neutrino capture cross…
Signal estimation in the presence of background noise is a common problem in several scientific disciplines. An 'On/Off' measurement is performed when the background itself is not known, being estimated from a background control sample. The…
We use the configuration interaction method and many-body perturbation theory to perform accurate calculations of energy levels, transition amplitudes, and lifetimes of low-lying states of barium and radium. Calculations for radium are…
We present an imaging algorithm for polarimetric interferometric data from radio telescopes. It is based on Bayesian statistics and thereby able to provide uncertainties and to incorporate prior information such as positivity of the total…
We demonstrate that the matter density distribution in the surface region is determined well by the use of the relatively low-intensity beams that become available at the upcoming radioactive beam facilities. Following the method used in…
Radio occultations are commonly used to assess remotely the thermodynamic properties of planets or satellites' atmospheres within the solar system. The data processing usually involves the so-called Abel inversion method or the numerical…
Exposure assessment models are deterministic models derived from physical-chemical laws. In real workplace settings, chemical concentration measurements can be noisy and indirectly measured. In addition, inference on important parameters…
We investigate Quantum Target Ranging in the context of multi-hypothesis testing and its applicability to real-world LiDAR systems. First, we demonstrate that ranging is generally an easier task compared to the well-studied problem of…
We used Bayesian methods to compare the predictions of probabilistic risk assessment -- the theoretical tool used by the nuclear industry to predict the frequency of nuclear accidents -- with empirical data. The existing record of accidents…
In this paper the Bayesian analysis is applied to assign a probability density to the value of a quantity having a definite sign. This analysis is logically consistent with the results, positive or negative, of repeated measurements.…
In this paper, quickest detection of false data injection attack on remote state estimation is considered. A set of $N$ sensors make noisy linear observations of a discrete-time linear process with Gaussian noise, and report the…