Speaker
Description
Dielectrons are an exceptional tool to study the evolution of the medium created in heavy-ion collisions. In central collisions, the energy densities are sufficient to create a quark-gluon plasma (QGP). At LHC energies, the dominant background process for the measurements of thermal e$^{+}$e$^{-}$ pairs originating from the QGP are correlated HF hadron decays which dominate the dielectron yield for invariant masses above 1.1 GeV/$c^2$. Their contribution is modified in the medium compared to elementary collisions to an unknown extent, leading to large uncertainties in the subtraction of known hadronic sources.
Alternatively, a topological separation can be utilised to disentangle them from the contribution of thermal dielectrons originating from the primary vertex.
As machine learning (ML) algorithms have achieved state-of-the-art performance in a variety of high-energy physics analyses, deep neural networks (DNNs) can be applied to capture the complex multidimensional correlations in the tracking parameters to identify these pairs.
In this poster, a DNN to classify dielectron sources based on their decay topology with the ALICE detector will be presented for simulated Pb--Pb collisions at $\sqrt{s_{\text{NN}}}=5.02$ TeV. Their performance will be compared to the established analysis on the distance-of-closes approach (DCA) to the primary vertex.
Finally, the way these ML techniques could be incorporated in future dielectron analysis will be discussed.
