High Energy / Nuclear Theory / RIKEN Seminars

[NT/RBRC seminar] Energy loss of QCD jets in heavy-ion collisions

by Konrad Tywoniuk (University of Bergen)




The quark-gluon plasma (QGP) created in high-energy heavy-ion collisions at RHIC and LHC is opaque to energetic and heavy particles that are created in short-distance particle scattering. Jets, aligned collections of energetic hadrons resulting from the fragmentation of fundamental quarks and gluons that are collected within a cone of radius R, are of special interest since they develop on time-scales comparable to the lifetime of the plasma. Jet ``quenching'', or the suppression of the jet yield at large transverse momentum, is therefore a probe not only of the elastic and inelastic interactions with the medium, but also of the medium's capability to resolve correlated QCD color charges. The energy removed from the jet is redistributed in modes that span hard collinear gluon radiation (bremsstrahlung) to softer excitations that ultimately thermalize with the surrounding medium.

The radius dependence of the jet spectrum is particularly sensitive to the rich physics outlined above. In the first part of the talk, I will present a recent calculation of the jet spectrum in heavy-ion collisions where the medium parameters are sampled from a realistic hydrodynamic evolution of the QGP. Up to relatively large radii R~0.6, the suppression is dominated by perturbative physics while non-perturbative effects, related to the details of thermalization, start to dominate at R~1. This provides, for the first time, a solid basis for higher-order precision calculations of the jet spectrum that are paramount to realize the potential of hard probes as precision tools to extract the properties of the QGP.

However, jets are rare events and their spectrum drops rapidly with transverse momentum. This induces a strong bias on any process happening in the medium and makes it hard to dig out rare events where heavy-ion jets were substantially modified. In the second part of my talk, I will report on a recent attempt to extract the jet energy before quenching using machine learning. This allows to reduce the biases and enhance the signal of medium modifications. It also allows to better constrain jets as tomographic tools of the medium.