Deep neural network techniques in the calibration of space-charge distortion fluctuations for the ALICE TPC

The Time Projection Chamber (TPC) of the ALICE experiment at CERN LHC was upgraded for Run 3 and 4. Readout chambers based on Gas Electron Multiplier (GEM) technology a new readout scheme allow continuous data taking highest interaction rates expected in Pb-Pb collisions. Due to absence gating grid system, significant amount ions created multiplication region is enter TPC drift volume distort uniform electric field that guides electrons pads. Analytical calculations were considered correct space-charge distortion fluctuations but they proved be too slow calibration reconstruction workflow 3. In this paper, we discuss novel strategy developed by Collaboration perform distortion-fluctuation corrections with machine learning convolutional neural network techniques. results preliminary studies are shown prospects further development optimization also discussed.