Tetris-inspired detector with neural network for radiation mapping

TL;DR

This paper introduces a novel Tetris-inspired neural network framework for radiation mapping that achieves high-resolution source localization using minimal detector pixels, enhancing environmental monitoring capabilities.

Contribution

The study presents a new detector design and machine learning approach that improves radiation source localization with fewer pixels and better performance than traditional methods.

Findings

High-resolution directional mapping with as few as four pixels.

Enhanced localization accuracy using MAP with a moving detector.

Tetris-shaped detectors outperform conventional grid-shaped detectors.

Abstract

In recent years, radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. In terms of both materials and their configurations, radiation detectors have been developed to locate the directions and positions of the radiation sources. In this process, algorithm is essential in converting detector signals to radiation source information. However, due to the complex mechanisms of radiation-matter interaction and the current limitation of data collection, high-performance, low-cost radiation mapping is still challenging. Here we present a computational framework using Tetris-inspired detector pixels and machine learning for radiation mapping. Using inter-pixel padding to increase the contrast between pixels and neural network to analyze the detector readings, a detector with as few as four pixels can achieve high-resolution directional mapping. By further imposing Maximum a Posteriori (MAP) with a moving detector, further radiation position localization is achieved. Non-square, Tetris-shaped detector can further improve performance beyond the conventional grid-shaped detector. Our framework offers a new avenue for high quality radiation mapping with least number of detector pixels possible, and is anticipated to be capable to deploy for real-world radiation detection with moderate validation.