2021 journal article

A hierarchical Bayesian model for background variation in radiation source localization

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT, 1002.

By: I. Michaud*, K. Schmidt n, R. Smith n‚ÄČ & J. Mattingly n

author keywords: Mixed-effects statistical model; Radiation detection; Inverse problem; Bayesian inference; Wide-area search
TL;DR: A hierarchical Bayesian model is presented that simultaneously infers background and source location parameters without requiring separate estimation of the background radiation at each detector location. (via Semantic Scholar)
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities (OpenAlex)
Source: Web Of Science
Added: June 10, 2021

In this paper, we apply a new model to account for varying background radiation in radiological source localization. We present a hierarchical Bayesian model that simultaneously infers background and source location parameters without requiring separate estimation of the background radiation at each detector location. We employ a simplified photon transport model to reduce the computational expense of Bayesian model calibration. We demonstrate the model accuracy by localizing a cesium-137 source in a simulated city block, and we analyze experimental field measurements with varying background. In both cases, the model provides sufficient fidelity that we can locate the source while simultaneously estimating background radiation.