Beam Performance of Tracking Detectors with Industrially Produced GEM Foils

Three Gas-Electron-Multiplier tracking detectors with an active area of 10 cm x 10 cm and a two-dimensional, laser-etched orthogonal strip readout have been tested extensively in particle beams at the Meson Test Beam Facility at Fermilab. These detectors used GEM foils produced by Tech-Etch, Inc. They showed an efficiency in excess of 95% and spatial resolution better than 70 um. The influence of the angle of incidence of particles on efficiency and spatial resolution was studied in detail.Comment: 8 pages, 9 figures, accepted by Nuclear Instruments and Methods in Physics Research

RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we cannot only form a decision on the spot, but also ponder, revisiting an initial guess from different angles, distilling relevant information, arriving at a better decision. Here, we propose RecycleNet, a latent feature recycling method, instilling the pondering capability for neural networks to refine initial decisions over a number of recycling steps, where outputs are fed back into earlier network layers in an iterative fashion. This approach makes minimal assumptions about the neural network architecture and thus can be implemented in a wide variety of contexts. Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision. We evaluate this across a variety of segmentation benchmarks and show consistent improvements even compared with top-performing segmentation methods. This allows trading increased computation time for improved performance, which can be beneficial, especially for safety-critical applications.Comment: Accepted at 2024 Winter Conference on Applications of Computer Vision (WACV