Reconstructing jet anisotropies with cumulants

In relativistic heavy-ion collisions, where quark-gluon plasma forms, hadron production is anisotropic at both low and high transverse momentum, driven by flow dynamics and spatial anisotropies. To better understand these mechanisms, we use multi-particle correlations to reconstruct jet anisotropies. We simulate data using \textsc{TennGen}\xspace as a hydro-like background and combine it with \textsc{Pythia-8}\xspace generated jets, clustering them with the anti-$k_{\mathrm{t}}$\xspace algorithm. Jet anisotropies are unfolded using a Bayesian technique, ensuring the robustness of the reconstructed signals. Our results demonstrate that multi-particle cumulant methods can accurately capture the differential jet azimuthal anisotropies, providing crucial insights into high-$p_{T}\xspace$ behavior and the dynamics within heavy-ion collisions.

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