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

ML Track Fitting in Nuclear Physics

Nov 5, 2019, 4:45 PM
Hall G (Adelaide Convention Centre)

Hall G

Adelaide Convention Centre

Oral Track 6 – Physics Analysis Track 6 – Physics Analysis


Thomas Britton (JLab)


Charged particle tracking represents the largest consumer of CPU resources in high data volume Nuclear Physics experiments. An effort is underway to develop ML networks that will reduce the resources required for charged particle tracking. Tracking in NP experiments represent some unique challenges compared to HEP. In particular, track finding typically represents only a small fraction of the overall tracking problem in NP. This presentation will outline the differences and similarities between NP and HEP charged particle tracking and areas where ML learning may provide a benefit. The status of the specific effort taking place at Jefferson Lab will also be shown.

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Primary authors

David Lawrence (Jefferson Lab) Dr Gagik Gavalian (Jefferson Lab) Thomas Britton (JLab)

Presentation materials