Speaker
Yao Zhang
Description
Drift chamber is the main tracking detector for high energy physics experiment like BESIII. Deep learning developments in the last few years have shown tremendous improvements in the analysis of data especially for object classification and parameter regression. Here we present a first study of deep learning architectures applied to BESIII Monte-carlo data to make estimation of the track parameter based on hit measurement of drift chamber.
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