Gradient Tomography of Jet Quenching in Heavy-Ion Collisions

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 collisions, this asymmetry with respect to a plane defined by the beam and trigger particle (photon, hadron or jet) with a given orientation relative to the event plane is shown to be closely related to the transverse position of the initial jet production in full event-by-event simulations within the linear Boltzmann transport model. Such a gradient tomography can be used to localize the initial jet production position for more detailed study of jet quenching and properties of the quark-gluon plasma along a given propagation path in heavy-ion collisions.Comment: 5 pages in RevTex with 4 figures, final published version in PR

Bayesian extraction of jet energy loss distributions in heavy-ion collisions

Based on the factorization in perturbative QCD, a jet cross sections in heavy-ion collisions can be expressed as a convolution of the jet cross section in $p+p$ collisions and a jet energy loss distribution. Using this simple expression and the Markov Chain Monte Carlo method, we carry out Bayesian analyses of experimental data on jet spectra to extract energy loss distributions for both single inclusive and $\gamma$-triggered jets in $Pb+Pb$ collisions with different centralities at two colliding energies at the Large Hadron Collider. The average jet energy loss has a dependence on the initial jet energy that is slightly stronger than a logarithmic form and decreases from central to peripheral collisions. The extracted jet energy loss distributions with a scaling behavior in $x=\Delta p_T /\langle \Delta p_T\rangle$ have a large width. These are consistent with the linear Boltzmann transport model simulations, in which the observed jet quenching is caused on the average by only a few out-of-cone scatterings.Comment: 5 pages in RevTex, 3 figures, final version to appear in Phys. Rev. Letter

Multiple jets and γ\gamma-jet correlation in high-energy heavy-ion collisions

$\gamma$-jet production is considered one of the best probes of the hot quark-gluon plasma in high-energy heavy-ion collisions since the direct $\gamma$ can be used to gauge the initial energy and momentum of the associated jet. This is investigated within the Linear Boltzmann Transport (LBT) model for jet propagation and jet-induced medium excitation. With both parton energy loss and medium response from jet-medium interaction included, LBT can describe experimental data well on $\gamma$-jet correlation in Pb+Pb collisions at the Large Hadron Collider. Multiple jets associated with direct $\gamma$ production are found to contribute significantly to $\gamma$-jet correlation at small $p_T^{\rm jet}<p_T^\gamma$ and large azimuthal angle relative to the opposite direction of $\gamma$. Jet medium interaction not only suppresses the leading jet at large $p_T^{\rm jet}$ but also sub-leading jets at large azimuthal angle. This effectively leads to the narrowing of $\gamma$-jet correlation in azimuthal angle instead of broadening due to jet-medium interaction. The $\gamma$-jet profile on the other hand will be broadened due to jet-medium interaction and jet-induced medium response. Energy flow measurements relative to the direct photon is illustrated to reflect well the broadening and jet-induced medium response.Comment: 11 pages with 12 figures, revised version includes discussions on the background subtraction and different definitions of jet profil

Feature-Rich Audio Model Inversion for Data-Free Knowledge Distillation Towards General Sound Classification

Data-Free Knowledge Distillation (DFKD) has recently attracted growing attention in the academic community, especially with major breakthroughs in computer vision. Despite promising results, the technique has not been well applied to audio and signal processing. Due to the variable duration of audio signals, it has its own unique way of modeling. In this work, we propose feature-rich audio model inversion (FRAMI), a data-free knowledge distillation framework for general sound classification tasks. It first generates high-quality and feature-rich Mel-spectrograms through a feature-invariant contrastive loss. Then, the hidden states before and after the statistics pooling layer are reused when knowledge distillation is performed on these feature-rich samples. Experimental results on the Urbansound8k, ESC-50, and audioMNIST datasets demonstrate that FRAMI can generate feature-rich samples. Meanwhile, the accuracy of the student model is further improved by reusing the hidden state and significantly outperforms the baseline method.Comment: Accepted by ICASSP 2023. International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023

E-by-e jet suppression, anisotropy, medium response and hard-soft tomography

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 $\gamma$-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 with the anisotropic flows of bulk hadrons from the underlying hydrodynamic events.Comment: 4 pages, 6 figures, Parallel talk presented at QM201

Deep learning assisted jet tomography for the study of Mach cones in QGP

Mach cones are expected to form in the expanding quark-gluon plasma (QGP) when energetic quarks and gluons (called jets) traverse the hot medium at a velocity faster than the speed of sound in high-energy heavy-ion collisions. The shape of the Mach cone and the associated diffusion wake are sensitive to the initial jet production location and the jet propagation direction relative to the radial flow because of the distortion by the collective expansion of the QGP and large density gradient. The shape of jet-induced Mach cones and their distortions in heavy-ion collisions provide a unique and direct probe of the dynamical evolution and the equation of state of QGP. However, it is difficult to identify the Mach cone and the diffusion wake in current experimental measurements of final hadron distributions because they are averaged over all possible initial jet production locations and propagation directions. To overcome this difficulty, we develop a deep learning assisted jet tomography which uses the full information of the final hadrons from jets to localize the initial jet production positions. This method can help to constrain the initial regions of jet production in heavy-ion collisions and enable a differential study of Mach-cones with different jet path length and orientation relative to the radial flow of the QGP in heavy-ion collisions