  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.

Instrumentation and Detectors;Machine Learning