Simulation of DAMPE silicon microstrip detectors in the Allpix 2 framework
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN: 0168-9002, Vol: 1057, Page: 168685
2023
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Silicon strip detectors have been widely utilized in space experiments for gamma-ray and cosmic-ray detections thanks to their high spatial resolution and stable performance. For a silicon micro-strip detector, the Monte Carlo simulation is recognized as a practical and cost-effective approach to verify the detector performance. In this study, a technique for the simulation of the silicon micro-strip detector with the Allpix 2 framework is developed. By incorporating the electric field into the particle transport simulation based on Geant4, this framework could precisely emulate the carrier drift in the silicon micro-strip detector. The simulation results are validated using the beam test data as well as the flight data of the DAMPE experiment, which suggests that the Allpix 2 framework is a powerful tool to obtain the performance of the silicon micro-strip detector.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0168900223006757; http://dx.doi.org/10.1016/j.nima.2023.168685; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85173571090&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0168900223006757; https://dx.doi.org/10.1016/j.nima.2023.168685
Elsevier BV
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