Application of machine learning techniques for the ALICE TPC space-charge distortion correction and for particle tracking in Si detectors

A Large Ion Collider Experiment (ALICE) is an experiment at the Large Hadron Collider (LHC) which aims to understand the most basic properties of Quantum Chromodynamics (QCD) by observing the quark-gluon plasma (QGP) created in relativistic heavy-ion collisions. The ALICE detector has been largely upgraded during the LHC Long Shutdown to become capable of collecting Pb-Pb collision data at an unprecedented interaction rate of 50 kHz. The Time Projection Chamber (TPC) is the main tracking detector of ALICE. Distortions of the electron drift paths caused by ion backflow from the readout chambers significantly affect the TPC measurements and therefore must be corrected in order to reach the intrinsic detector resolution. The most challenging aspect of the correction is posed by the calibration of distortion fluctuations relevant on time scales in the order of 10 ms. A framework for the distortion fluctuation correction using machine learning techniques is under development and its current status will be discussed.

Another atempt to use machine learning techniques for particle tracking using multi-layer silicon pixel detectors is also in progress. The status of its development will also be discussed.