Leading jets and energy loss

Abstract

The formation and evolution of leading jets can be described by jet functions which satisfy non-linear DGLAP-type evolution equations. Different than for inclusive jets, the leading jet functions constitute normalized probability densities for the leading jet to carry a longitudinal momentum fraction relative to the initial fragmenting parton. We present a parton shower algorithm which allows for the calculation of leading-jet cross sections where logarithms of the jet radius and threshold logarithms are resummed to next-to-leading logarithmic (NLL′) accuracy. By calculating the mean of the leading jet distribution, we are able to quantify the average out-of-jet radiation, the so-called jet energy loss. When an additional reference scale is measured, we are able to determine the energy loss of leading jets at the cross section level which is identical to parton energy loss at leading-logarithmic accuracy. We identify several suitable cross sections for an extraction of the jet energy loss and we present numerical results for leading subjets at the LHC. In addition, we consider hemisphere and event-wide leading jets in electron-positron annihilation similar to measurements performed at LEP. Besides the average energy loss, we also consider its variance and other statistical quantities such as the KL divergence which quantifies the difference between quark and gluon jet energy loss. We expect that our results will be particularly relevant for quantifying the energy loss of quark and gluon jets that propagate through hot or cold nuclear matter.

Deep Learning for the classification of quenched jets

“…Dedicated HI jet observables have been proposed as being more resilient to such medium response component [8], while being, simultaneously, calculable in a pQCD prescription [21]. Nonetheless, some of the currently available jet substructure measurements [22,23] do not enjoy this feature, and part of the unrelated underlying event can lead to effects very similar to those that can be argued to arise from medium response contribution to jets [24].…”

Jet Substructure and Correlations in Hadronic Final States from ALICE

“…Subjet fragmentation. In heavy-ion collisions, measurements of reclustered subjets have been proposed as sensitive probes of jet quenching [19,24,25]. We consider first inclusively clustering jets with the anti-T jet algorithm and jet radius , and then reclustering the jet constituents with the anti-T jet algorithm and subjet radius < .…”

Jet substructure and correlations in hadronic final states from ALICE

“…Subjet fragmentation. In heavy-ion collisions, measurements of reclustered subjets have been proposed as sensitive probes of jet quenching [19,24,25]. We consider first inclusively clustering jets with the anti-𝑘 T jet algorithm and jet radius 𝑅, and then reclustering the jet constituents with the anti-𝑘 T jet algorithm and subjet radius 𝑟 < 𝑅.…”