Speaker
Description
We present a machine learning approach for the simultaneous extraction of anisotropic flow harmonics (v₁–v₆) and jet amplitudes from two-particle angular correlation functions in heavy-ion collisions. A multi-layer perceptron (MLP) neural network is trained on HIJING-like toy model simulations incorporating realistic physics ingredients, including collective flow parametrizations from ALICE data, jet fragmentation functions, thermal background spectra, and resonance decay contributions. The network takes as input 2D correlation histograms in (Δφ, Δη) space along with event-level observables (centrality, trigger and associated particle transverse momenta) and outputs the flow harmonic coefficients and near-side/away-side jet yields. The model is validated against ALICE HEPData measurements of trigger-associated correlations in 0–10% central Pb-Pb collisions at √(〖s_NN〗_ ) = 2.76 TeV with trigger pt in the range 8–16 GeV/c and associated pt between 1–6 GeV/c. We demonstrate that the MLP successfully decomposes the raw correlation function into its flow-modulated background and jet contributions, providing a model-independent alternative to traditional Fourier decomposition and template fitting techniques. The inclusion of non-linear hydrodynamic mode coupling for higher-order harmonics (v₄, v₅, v₆) is shown to be essential for accurate description of the data. This approach opens new possibilities for automated, high-precision extraction of jet quenching observables in the presence of strong collective flow backgrounds.