TY  - JOUR
A1  - Gorbunov, Sergey
A1  - Hellbär, Ernst
A1  - Innocenti, Gian Michele
A1  - Ivanov, Marian
A1  - Kabus, Maja Jadwiga
A1  - Kleiner, Matthias
A1  - Riaz, Haris
A1  - Rohr, David
A1  - Sadikin, Rifki
A1  - Schweda, Kai
A1  - Sekihata, Daiki
A1  - Shahoyan, Ruben
A1  - Völkel, Benedikt
A1  - Wiechula, Jens
A1  - Zampolli, Chiara
A1  - Appelshäuser, Harald
A1  - Büsching, Henner
A1  - Graczykowski, Łukasz Kamil
A1  - Große-Oetringhaus, Jan Fiete
A1  - Hristov, Peter
A1  - Gunji, Taku
A1  - Masciocchi, Silvia
T1  - Deep neural network techniques in the calibration of space-charge distortion fluctuations for the ALICE TPC
T2  - EPJ Web of Conferences
N2  - 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.
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/70059
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-700593
SN  - 2100-014X
VL  - 251
IS  - 03020
PB  - EDP Sciences
CY  - Les Ulis
ER  -