The 9th Asian Triangle Heavy-Ion Conference (ATHIC 2023)

Forward muon track reconstruction between multiple detectors using machine learning in ALICE Run 3

25 Apr 2023, 17:00

20m

Multi-Purpose Studio, 2nd Floor (JMS Aster Plaza)

Poster Experimental techniques and future programs Poster Session

Speaker

Mr Ren Ejima (Hiroshima University (JP))

Description

A new detector was installed in ALICE in the forward region during LHC LS2 with the aim to improve the accuracy of the dimuon opening angle measurement more than ever since the LHC Run 3. Such new detector cannot identify muons and measure their momentum, so it must be used in combination with an existing detector. Therefore, it is necessary to correctly match the tracks reconstructed by each detector. However, the huge amount of tracks due to high-multiplicity events such as HIC and the Coulomb multiple scattering inside the thick layer of material between the new and existing detectors for muon identification pose challenges. In this talk, we will show how machine learning can be used to correctly match these tracks and evaluate their performance using purity and efficiency. We will also discuss results obtained applyng machine learning techniques to the reconstruction of the invariant mass distributions.