November 28, 2023 to December 1, 2023
Catholic University of America, Washington D.C.
US/Eastern timezone
Artificial Intelligence for the Electron Ion Collider

Object Condensation for Track Building in a Backward Electron Tagger at the EIC

Nov 28, 2023, 10:40 AM
25m

Speaker

Dr Simon Gardner (University of Glasgow)

Description

Quasi-real photoproduction measurements at the Electron Ion Collider will require a far backward electron tagger to detect electrons scattered at small angles close to the beam line. A high occupancy is expected in the electron tagger, leading to many possible permutations of hits deposited by the electrons into single electron tracks. To avoid a slow and computationally expensive combinatorial approach to track building, machine learning algorithms such as object condensation methods can be used to recognise objects such as tracks from hits in a detector. We demonstrate how these object condensation methods are particularly well suited to track building in the far backward electron tagger, achieving an efficiency in track finding at or above 95% and a purity at or above 90%, in the presence of noise and hit detection inefficiencies.

Primary authors

Derek Glazier Dr Richard Tyson (University of Glasgow) Dr Simon Gardner (University of Glasgow) Kenneth Livingston (University of Glasgow)

Presentation materials