NewsEnhanced deep-learning approach to spatiotemporal multi-hit reconstruction with delay-line detectors – new publication in Phys. Rev. Applied
In a recent study published in Phys. Rev. Applied, researchers from FAU, the University of Alabama, and LMU report an enhanced deep learning pipeline that substantially improves spatio-temporal multi-hit reconstruction in Delay-Line Detectors. By integrating cross-channel peak detection and novel, self-sufficient peak matching models, the team demonstrates a superior ability to resolve overlapping signals from […]In a recent study published in Phys. Rev. Applied, researchers from FAU, the University of Alabama, and LMU report an enhanced deep learning pipeline that substantially improves spatio-temporal multi-hit reconstruction in Delay-Line Detectors. By integrating cross-channel peak detection and novel, self-sufficient peak matching models, the team demonstrates a superior ability to resolve overlapping signals from […]
