A quark-gluon plasma (QGP) that emerges in collisions of ultra-relativistic heavy ions can be probed by jet. Jets are the spray of collimated showers of high 𝑝T hadrons resulting from fragmentation of highly-virtual partons after a hard scattering. The jet origin parton interacts with the QGP via collisional and radiative processes that lead to a phenomenon known as a jet quenching which manifests itself by suppression of high-𝑝T jet yields and jet shape modifications. The observed modifications carry information about the transport properties of the QGP.
In this presentation, we report the nuclear modification factor measurements of full jets in Pb-Pb collisions at √𝑠NN = 5.02 TeV taken with the LHC-ALICE experiment. The jet energy scale is corrected for a fluctuation of the underlying event with the area-based method, where the underlying event density is obtained either with the traditional area-based method or machine learning-based estimators. The machine learning estimator enables to access of lower transverse momenta and larger jet radii than that in the area-based method. The potential bias introduced by the machine learning method is investigated and its impact is quantified.