EPJ Web Conf.
Volume 251, 202125th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
|Number of page(s)||10|
|Published online||23 August 2021|
Deep neural network techniques in the calibration of space-charge distortion fluctuations for the ALICE TPC
1 Frankfurt Institute for Advanced Studies
2 GSI - Helmholtzzentrum für Schwerionenforschung GmbH
3 European Organization for Nuclear Research (CERN)
4 Faculty of Mathematics and Information Science, Warsaw University of Technology
5 Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt
6 National University of Sciences & Technology, Islamabad
7 Indonesian Institute of Sciences
8 University of Tokyo
9 Physikalisches Institut, Ruprecht-Karls-Universitat Heidelberg
10 Faculty of Physics, Warsaw University of Technology
* e-mail: email@example.com
Published online: 23 August 2021
The Time Projection Chamber (TPC) of the ALICE experiment at the CERN LHC was upgraded for Run 3 and Run 4. Readout chambers based on Gas Electron Multiplier (GEM) technology and a new readout scheme allow continuous data taking at the highest interaction rates expected in Pb-Pb collisions. Due to the absence of a gating grid system, a significant amount of ions created in the multiplication region is expected to enter the TPC drift volume and distort the uniform electric field that guides the electrons to the readout pads. Analytical calculations were considered to correct for space-charge distortion fluctuations but they proved to be too slow for the calibration and reconstruction workflow in Run 3. In this paper, we discuss a novel strategy developed by the ALICE Collaboration to perform distortion-fluctuation corrections with machine learning and convolutional neural network techniques. The results of preliminary studies are shown and the prospects for further development and optimization are also discussed.
© The Authors, published by EDP Sciences, 2021
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