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- W3177940141 abstract "Waveform sampling systems are used pervasively in the design of front endelectronics for radiation detection. The introduction of new feature extractionalgorithms (eg. neural networks) to waveform sampling has the great potentialto substantially improve the performance and enrich the capability. To analyzethe limits of such algorithms and thus illuminate the direction of resolutionoptimization, in this paper we systematically simulate the detection procedureof contemporary radiation detectors with an emphasis on pulse timing. neuralnetworks and variants of constant fraction discrimination are studied in a widerange of analog channel frequency and noise level. Furthermore, we propose anestimation of multivariate Cram'er Rao lower bound within the model usingintrinsic-extrinsic parameterization and prior information. Two case studies(single photon detection and shashlik-type calorimeter) verify the reliabilityof the proposed method and show it works as a useful guideline when assessingthe abilities of various feature extraction algorithms." @default.
- W3177940141 created "2021-07-19" @default.
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- W3177940141 date "2021-05-31" @default.
- W3177940141 modified "2023-09-25" @default.
- W3177940141 title "Simulation study of neural network--featured timing systems for radiation detectors: performance evaluation based on bound analysis" @default.
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