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["Soltz, Ron" <soltz1 AT llnl.gov>]

Hi Ross,

 

Here’s a draft of the ML distortion abstract for QM.  Feel free to make any edits before passing along to Marzia.

 

-Ron

 

Distortions in the sPHENIX TPC using Digital Current with Machine Learning

 

The Time Projection Chamber (TPC) to be used for tracking and particle identification in the sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC) is expected to experience significant distortions from build backflow of ions created by the combination of high collision rates and amplification from Gas Electron Multiplier (GEM).   Digital current readouts can be used to reconstruct the ion space charge density to calculate the electric and magnetic field distortions in the chamber, but at significant computational cost.  Machine learning methods provide a mechanism to reduce this computational cost while also reducing errors by training and validating with experimental data.  We will present methods and results using machine learning techniques to predict and correct for space-charge induced distortions in the sPHENIX TPC.