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Ultimate position resolution of pixel clusters with binary readout for particle tracking
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The optimal use of silicon pixel charge information for particle identification
Particle identification using the energy loss in silicon detectors is a powerful technique for probing the Standard Model (SM) as well as searching for new particles beyond the SM. Traditionally, such techniques use the truncated mean of the energy loss on multiple layers, in order to mitigate heavy tails in the charge fluctuation distribution. We show that the optimal scheme using the charge in multiple layers significantly outperforms the truncated mean. Furthermore, truncation itself does not significantly degrade performance and the optimal classifier is well-approximated by a linear combination of the truncated mean and truncated variance.
Gamma-ray tracking for high energy gamma-ray imaging in pixelated CdZnTe
Sequencing gamma-ray interactions within a detector system is an integral component of Compton imaging. In detectors with poor timing resolution compared to the time interval of successive interactions, algorithms which order gamma-ray interactions must be implemented using only energy and position information. This work examines previous algorithms and inspects interaction kinematics to increase the sequencing algorithm’s speed and effectiveness. The proposed method, in which the first interaction is assumed to deposit the largest energy, has improved sequencing performance by greater than 20% for full energy gamma ray depositions larger than 1 MeV that do not contain pair-production. In addition, the algorithm shows a decrease in computational costs for sequence reconstruction to allow for better real time reconstruction. Experimental results show an almost twofold increase in the signal to noise ratio (SNR) for simple backprojection images of a 22 Na source. Additional measurements of the 2.2 MeV gamma rays from H 1 (n,$\gamma$)D 2 neutron capture demonstrates the proposed algorithm’s superior performance.
Measured effectiveness of deep N-well substrate isolation in a 65 nm pixel readout chip prototype
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Simulation of charge drift in surface doped, pixelated micro-structured semiconductor neutron detectors
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Radiation damage of 2 × 2 × 1 cm 3 pixelated CdZnTe due to high-energy protons
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High-precision 4D tracking with large pixels using thin resistive silicon detectors
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All-printed full-color pixel organic photodiode array with a single active layer
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From pixels to patterns: Coupling Optical Coherence Tomography and machine learning for monitoring coastal wetland root systems
Coastal wetlands are crucial in shoreline stabilization, carbon sequestration, and storm protection. Yet, due to limitations in traditional destructive sampling techniques, the belowground biomass (live root mass) and necromass (dead and decaying roots) remain difficult to assess in coastal wetlands, limiting our understanding on coastal resilience, nutrient cycling, and soil structure. This study employs Optical Coherence Tomography (OCT) as a high-resolution imaging technique to analyze root biomass and necromass in the Terrebonne Basin, Louisiana. A Random Forest (RF) model was developed to classify root health states based on OCT-derived features, achieving an accuracy of 70% in distinguishing live from dead root segments. The results demonstrate that OCT, combined with ML, offers a promising novel approach to root analysis, providing fine-scale insights into root morphology and decay patterns that are not easily captured by conventional methods. This research lays the foundation for future integration of OCT with complementary imaging modalities such as X-ray Computed Tomography (XCT) and advanced ML algorithms to enhance classification accuracy and scalability. Future work aims to expand the dataset diversity across different wetland types and apply the methodology for large-scale, repeatable assessments of root biomass turnover and accumulation, with important implications for wetland monitoring, conservation, and restoration under changing environmental conditions.