2020
DOI: 10.1051/epjconf/202024506015 View full text |Buy / Rent full text
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Abstract: Charged particle tracking represents the largest consumer of CPU resources in high data volume Nuclear Physics (NP) experiments. An effort is underway to develop machine learning (ML) networks that will reduce the resources required for charged particle tracking. Tracking in NP experiments represent some unique challenges compared to high energy physics (HEP). In particular, track finding typically represents only a small fraction of the overall tracking problem in NP. This presentation will outline the differ… Show more

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