Jets in relativistic heavy-ion collisions interact with the quark-gluon plasma (QGP), leading to
effects such as a suppression of jet yields and modification of internal jet structure that can be used
to constrain properties of the QGP. The dependence of jet suppression on the resolution parameter
(R) and jet pT is a useful observable to disentangle competing energy loss mechanisms with a high
discriminating power when compared to models. Due to the presence of the large underlying event
in heavy-ion collisions, measurements at large R and low pT are limited by the pT resolution using
traditional techniques. A new method using machine learning techniques is used to correct the large
background in heavy-ion collisions and extend the measurement of inclusive jet yields to lower pT
than previously achieved in heavy-ion collisions at the LHC. This talk will present the inclusive
jet nuclear modification factors in Pb–Pb collisions in various centrality classes at √sNN = 5.02
TeV recorded with the ALICE detector for resolution parameters up to R = 0.6 for jet transverse
momenta down to 40 GeV/c. These results suggest that jets reconstructed with larger resolution
parameters are more suppressed. Comparisons with jet quenching models will also be shown.
Recording:
https://bnl.zoomgov.com/rec/share/ssC6KD-44P6V2J9Kq9uS3PUbt8bF8Rl135jxFmVZC7uUS0azfbTF7jJiofPtk9-d.Ypf6X43vFsNtmssS?startTime=1652366681000
Passcode: qGk3XfR@