Nov 4 – 8, 2019
Adelaide Convention Centre
Australia/Adelaide timezone

BESIII drift chamber tracking with machine learning

Nov 7, 2019, 3:30 PM
Hall F (Adelaide Convention Centre)

Hall F

Adelaide Convention Centre

Poster Track 2 – Offline Computing Posters


Yao Zhang


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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Primary authors

Yao Zhang Mr Ye Yuan (Institute of High Energy Physics) Ms Qiumei Ma (Institute of High Energy Physics)

Presentation materials