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Machine learning based event reconstruction for the MUonE experiment

Zdybał Miłosz, Kucharczyk Marcin, Wolter Marcin

Computer Science

2024

T. 25 (1)

147--167

A proof-of-concept solution based on the machine learning techniques has beenimplemented and tested within the MUonE experiment designed to search forNew Physics in the sector of anomalous magnetic moment of a muon. Theresults of the DNN based algorithm are comparable to the classical reconstruc-tion, reducing enormously the execution time for the pattern recognition phase.The present implementation meets the conditions of classical reconstruction,providing an advantageous basis for further studies.

HBT analysis in ALICE with ITS stand-alone and combined neural tracking (preliminary results)

Badala A., Barbera R., Lo Re G., Palmeri A., Pappalardo G., Pulvirenti A., Riggi F.

Nukleonika

2004

Vol. 49,suppl.2

95-98

A neural network based algorithm to perform track recognition in the ALICE Inner Tracking System (ITS) for high transverse momentum particles (pt > 1 GeV/c) is presented. The model is based on the Denby-Peterson scheme, with some original improvements which are necessary to cope with the large track density expected at ALICE. Results are shown for central Pb-Pb events at 5.5 ATeV in the center of mass system and the comparison with the Kalman filter results is included. Data coming from this tracking procedure are used for 1-dimensional HBT correlations and results are presented.