Object condensation for track building in a backward electron tagger at the EIC

Abstract

At the Electron Ion Collider, quasi-real photoproduction measurements involve tracking scattered electrons at small angles relative to the beamline. These electrons act as effective beams of tagged almost-real photons, with a high flux compared to larger Q2 interactions. However, the proximity of the detector to the electron beam results in a very high flux of electrons from the bremsstrahlung process (about 10 electrons per 12 ns electron/ion bunch crossing over an area of approximately 100 cm2). Consequently, the tracking detector systems experience high occupancy. To address this, we propose using machine learning algorithms, specifically object condensation methods, which excel at track building in the quasi-real photon tagger. These algorithms achieve track finding efficiency of 95% or higher and purity of 90% or higher, even in the presence of noise and hit detection inefficiencies.