Deep learning techniques have the power to identify the degree of modification of high energy jets traversing deconfined QCD matter on a jet-by-jet basis. Such knowledge allows us to study jets based on their initial, rather than final energy. We show how this new technique provides unique access to the genuine configuration profile of jets over the transverse plane of the nuclear collision, both with respect to their production point and their orientation. Effectively removing the selection biases induced by final-state interactions, one can in this way analyse the potential azimuthal anisotropies of jet production associated to initial-state effects. Additionally, we demonstrate the capability of our new method to locate with precision the production point of a dijet pair in the nuclear overlap region, in what constitutes an important step forward towards the long term quest of using jets as tomographic probes of the quark-gluon plasma.
With deep learning techniques, the degree of modification of energetic jets that traversed hot QCD medium can be identified on a jet-by-jet basis. Due to the strong correlations between the degree of jet modification and its traversed length in the medium, we demonstrate the power of our novel method to locate the creation point of a dijet pair in the nuclear overlap region. In particular, jet properties, such as jet width and orientation can serve as additional handles to locate the creation points to a higher level of precision, which constitutes a significant development towards the long-st…
Diffusion wake is an unambiguous part of the jet-induced medium response in high-energy heavy-ion collisions that leads to a depletion of soft hadrons in the opposite direction of the jet propagation. New experimental data on $Z$-hadron correlation in Pb+Pb collisions at the Large Hadron Collider show, however, an enhancement of soft hadrons in the direction of both the $Z$ and the jet. Using a coupled linear Boltzmann transport and hydro model, we demonstrate that medium modification of partons from the initial multiple parton interaction (MPI) gives rise to a soft hadron enhancement that is …
The Linear Boltzmann Transport (LBT) model for jet propagation and interaction in quark-gluon plasma (QGP) has been used to study jet quenching in high-energy heavy-lion collisions. The suppression of single inclusive jet production, medium modification of $γ$-jet correlation, jet profiles and fragmentation functions as observed in experiments at Large Hadron Collider (LHC) can be described well by LBT in which jet-induced medium response is shown to play an essential role. In event-by-event simulations of jet quenching within LBT, jet azimuthal anisotropies are found to correlate linearly wit…
We propose a new framework, merging High Energy Factorization with final-state jet quenching effects due to interactions in a quark-gluon plasma, to compute di-jet rates at mid- and forward rapidity. It allows to consistently study the interplay of initial-state effects with medium interactions, opening the possibility for understanding the dynamics of hard probes in heavy-ion collisions and the QGP evolution in rapidity.
We illustrate with both a Boltzmann diffusion equation and full simulations of jet propagation in heavy-ion collisions within the Linear Boltzmann Transport (LBT) model that the spatial gradient of the jet transport coefficient perpendicular to the propagation direction can lead to a drift and asymmetry in the transverse momentum distribution. Such an asymmetry depends on both the spatial position along the transverse gradience and the propagating length. It can be used to localize the initial jet production positions for more detailed studies of jet quenching and properties of the quark-gluon…
Transverse momentum broadening and energy loss of a propagating parton are dictated by the space-time profile of the jet transport coefficient $\hat q$ in a dense QCD medium. The spatial gradient of $\hat q$ perpendicular to the propagation direction can lead to a drift and asymmetry in parton transverse momentum distribution. Such an asymmetry depends on both the spatial position along the transverse gradient and path length of a propagating parton as shown by numerical solutions of the Boltzmann transport in the simplified form of a drift-diffusion equation. In high-energy heavy-ion collisio…
When an energetic parton traversing the QCD medium, it may suffer multiple scatterings and lose energy. This jet quenching phenomenon may lead to the suppression of leading hadron productions as well as medium modifications of full jet observables in heavy-ion collisions. In this talk we discuss the nuclear modification factors and yield ratios of identified meson such as $η$, $ρ^0$, $ϕ$, $ω$, and $K^0_{\rm S}$ as well as $π$ meson at large $p_T$ in A+A collisions at the next-to-leading order (NLO) with high-twist approach of parton energy loss. Then we discuss a newly developed formalism of c…
Suppression of jet spectra or jet quenching in high-energy heavy-ion collisions is caused by jet energy loss in the dense medium. The azimuthal anisotropy of jet energy loss in non-central heavy-ion collisions can lead to jet anisotropy which in turn can provide insight into the path-length dependence of jet quenching. This is investigated within the Linear Boltzmann Transport (LBT) model which simulates both elastic scattering and medium-induced gluon radiation based on perturbative QCD for jet shower and medium recoil partons as well as radiated gluons as they propagate through the quark-glu…
We study the phenomenon of jet quenching utilizing quark and gluon jet substructures as independent probes of heavy ion collisions. We exploit jet and subjet features to highlight differences between quark and gluon jets in vacuum and in a medium with the jet-quenching model implemented in JEWEL. We begin with a physics-motivated, multivariate analysis of jet substructure observables including the jet mass, the radial moments, the $p_T^D$ and the pixel multiplicity. In comparison, we employ state-of-the-art image-recognition techniques by training a deep convolutional neutral network on jet im…
