Fig. 1: Overview of Tetris-inspired radiation mapping with neural networks.
From: Tetris-inspired detector with neural network for radiation mapping
a The geometrical setting of the radiation detectors. Instead of using a detector with a large square grid, here we use small 2 × 2 square and other Tetromino shapes. Padding material is added between each pixel to increase contrast. b–d The workflow for learning the radiation directional information with Tetris-shaped detector and machine learning. b Monte Carlo simulation is performed to generate the detector readings for various source directions. c The detector’s readouts are embedded to a matrix of filter layers for better distinguishing far-field and near-field scenarios. The embedded data then goes through a deep U-net. d The predicted direction of radiation sources from the U-net (brown) with predicted angular \(\hat{\theta }\) is compared to the ground-truth Monte Carlo simulations (blue) with true angular data θ. The prediction loss is calculated by comparing the pairs (θ, \(\hat{\theta }\)).
