arXiv subjects

Zhong Yang

Publications and source records attributed to Zhong Yang.

At least 19 recordsLinked to original sources

arXiv2026

The elliptic wind on jet wakes in high-energy heavy-ion collisions

Energy loss by fast partons induces a Mach-cone-like medium response as they propagate inside the hot quark-gluon plasma (QGP) in high-energy heavy-ion collisions. Because the QGP is nonuniform and its initial gradients generate collective flow, jet-induced medium response in this evolving system is also distorted by the flow and density gradient. This distortion leads to a broadened jet wake whose transverse width depends on the azimuthal angle of the jet propagation due to the elliptic anisotropy of the density gradient and the flow velocity in noncentral heavy-ion collisions. We propose and calculate the difference between the azimuth-dependent jet-hadron correlations for soft charged hadrons in in-plane and out-plane $\gamma$-jets as a measure of the elliptic broadening of the wake front and the deepening of the diffusion wake. We also study the sensitivity of this observable to the shear viscosity of the QGP. Experimental measurements of the azimuthal modulation of jet wakes induced by the wind of the elliptic flow at RHIC and LHC can provide additional constraints on the transport properties of the QGP.

nucl-th↗

arXiv2026

Energy-energy correlators inside single inclusive jets in heavy-ion collisions with CoLBT-hydro model

The energy-energy correlator (EEC) inside jets is a sensitive observable for studying jet modification in the quark-gluon plasma (QGP). However, its interpretation in heavy-ion collisions remains challenging, requiring a consistent understanding of jet evolution across multiple dynamical scales together with a proper treatment of the background subtraction. In this work, we employ an updated CoLBT-hydro framework in which a medium scale $Q_M$ = 2.0 GeV is introduced to separate the vacuum and in-medium stages of the parton shower, enabling a more self-consistent treatment of jet evolution. Using a theoretical background subtraction within the model, the resulting simulation reproduces the recent CMS measurement of the in-jet EEC, and through a decomposition of different contributions, highlights the impact of medium modification on the observable. To further validate the experimental procedure, we also implement the CMS mixed-event background-subtraction method directly in the simulation and find the results are consistent with those obtained with the theoretical background subtraction. Using $p_T$-ranked jets in each event, we further investigate the dependence of medium modification on the in-medium path length, reflected in the different EECs of leading and sub-leading jets. Finally, we explore the dependence of the leading-jet EEC on the dijet rapidity gap as a signal of the jet-induced diffusion wake.

hep-ph↗

arXiv2026

Study of jet-induced hydro response in high-energy heavy-ion collisions with a flow-matching generative model

In high-energy heavy-ion collisions, propagation of the energy deposited into the medium by energetic partons that traverse the quark-gluon plasma (QGP) leads to Mach-cone-like jet-induced medium response. Event-by-event simulations of jet-induced medium responses within a complete model such as the coupled Linear Boltzmann Transport and hydrodynamic (CoLBT-hydro) model are very resource-intensive. In this study, we develop a flow matching generative model trained by CoLBT-hydro events for the study of the medium response induced by $\gamma$-jets in high-energy heavy-ion collisions. With only the initial spatial and momentum information of the $\gamma$ and jets, the generative model is shown to conditionally reproduce the marginal final-state hadron spectra from the jet-induced hydro response in $0-10\%$ Pb+Pb collisions at $\sqrt{s_{\rm{NN}}}$ = 5.02~TeV. The generative model achieves a computational acceleration of approximately six orders of magnitude compared to the full CoLBT-hydro simulations, while faithfully preserving the statistical properties of the front and the diffusion wake of the Mach-cone-like hydro response and their contributions to the hadron spectra. Hadron spectra from the medium response, correlations between the front and diffusion wake and rapidity asymmetry due to the diffusion wake in $\gamma$-hadron correlation are further studied within the generative model.

nucl-th↗

arXiv2026

Probing Jet-Medium Interactions in Heavy-Ion Collisions Using Energy-Energy Correlators

Energy-energy correlators (EECs) provide a sensitive probe of both perturbative and nonperturbative dynamics in relativistic heavy-ion collisions. Jet-medium interactions enhance particle multiplicity within the jet cone, which must be properly accounted for when extracting the EEC of jet shower hadrons in experiments. To address this issue, we develop an augmentation method that exploits momentum conservation between the near-side and away-side regions, using $\gamma$-jet events with 0-10\% centrality in Pb+Pb collisions at $\sqrt{s_{NN}} = 5.02$ TeV simulated with the CoLBT-hydro model. This approach yields an experimentally reconstructed EEC that shows improved agreement with the EEC of hadrons originating primarily from jet parton splittings. Comparing EECs of jets from Pb+Pb and p+p collisions with different matching conditions can be sensitive to jet medium interaction dynamics, and provide a novel means to test the scenario of jet energy loss in the QGP, followed by fragmentations outside the QGP.

nucl-th↗

arXiv2026

Machine learning Hamiltonian enables scalable and accurate defect calculations: The case of oxygen vacancies in amorphous SiO$_2$

Point defects critically influence the properties of materials and devices, yet density functional theory (DFT) remains computationally demanding for defect supercell calculations. Machine learning interatomic potentials (MLIPs) offer high efficiency but require extensive datasets. MLIPs trained only on defect configurations in small supercells exhibit systematic energy errors in larger supercells, demonstrating limited transferability. Here, we present a machine learning Hamiltonian (MLH) model-based method for calculating total energies and atomic forces in defect supercells with linear-scaling computational cost, enabling efficient structural relaxation and accurate formation energy predictions. We take oxygen vacancies in amorphous SiO$_2$ as an example and train the MLH model on defect configurations in 95-atom supercells, with the training data derived from 120 self-consistent field calculations and 12 structural relaxations. The MLH model enables efficient structural relaxations for host (defect-free) and defect systems in larger supercells, avoiding the systematic energy errors observed in MLIPs. The cancellation of energy errors between host and defect systems yields accurate formation energy predictions, with deviations from DFT below 50 meV. The proposed method holds significant potential for defect simulations in complex materials.

cond-mat.mtrl-sci↗

arXiv2026

Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education

Orofacial clefts are among the most common congenital craniofacial abnormalities, yet accurate prenatal detection remains challenging due to the scarcity of experienced specialists and the relative rarity of the condition. Early and reliable diagnosis is essential to enable timely clinical intervention and reduce associated morbidity. Here we show that an artificial intelligence system, trained on over 45,139 ultrasound images from 9,215 fetuses across 22 hospitals, can diagnose fetal orofacial clefts with sensitivity and specificity exceeding 93% and 95% respectively, matching the performance of senior radiologists and substantially outperforming junior radiologists. When used as a medical copilot, the system raises junior radiologists' sensitivity by more than 6%. Beyond direct diagnostic assistance, the system also accelerates the development of clinical expertise. A pilot study involving 24 radiologists and trainees demonstrated that the model can improve the expertise development for rare conditions. This dual-purpose approach offers a scalable solution for improving both diagnostic accuracy and specialist training in settings where experienced radiologists are scarce.

cs.CV↗

arXiv2025

Living on the edge: radius effects in the angular substructure of heavy-ion jets

Jet substructure observables serve as essential tools for probing the quark-gluon plasma produced in relativistic heavy-ion collisions. Their interpretation, however, is often complicated by edge effects, which arise when correlated particles fall outside the reconstructed jet radius, introducing distortions that obscure the underlying QCD dynamics. In this work, we present a comprehensive phenomenological study of edge effects in soft-insensitive angular observables, taking the two-point energy correlator (EEC) as a representative example. We argue that these distortions scale linearly with the average angular separation between the winner-take-all and $E$-scheme axes $\langle \phi \rangle$, and validate this behavior across proton-proton (p-p) simulations with Pythia8 and Herwig7, as well as lead-lead (Pb-Pb) simulations using JEWEL and CoLBT. In p-p collisions, edge effects are strongly suppressed, scaling as $(R_L/R)^4$, whereas medium-modified jets can exhibit larger distortions, with contributions scaling as $(R_L/R)^2$ and $(R_L/R)^4$. Taking Pb-Pb/p-p ratios of the EEC substantially reduces, but does not completely eliminate, these distortions, highlighting the need of accounting for edge effects in the interpretation of heavy-ion jet substructure measurements. Since edge effects are largely governed by the $\langle \phi \rangle$ distribution, studying this distribution provides a new handle for benchmarking and constraining the modeling of edge effects in heavy-ion event generators.

hep-ph↗

arXiv2025

3D Structure of Jet-induced Diffusion Wake

Jet-induced diffusion wake in heavy-ion collisions has a unique 3D structure as manifested in the jet-hadron correlations in rapidity and azimuthal angle. The rapidity asymmetry observable in dijets provides a robust measurement of the diffusion wake because it essentially background-free.

hep-ph↗

arXiv2025

Diffusion wake: a distinctive consequence of the Mach-cone wake induced by supersonic jets in high-energy heavy-ion collisions

In this Research Perspective, we briefly review the diffusion wake, a distinctive consequence of the Mach-cone wake induced by the supersonic jets in ultra-relativistic heavy-ion collisions. The diffusion wake depletes soft hadrons in the direction opposite to the propagating jet. According to coupled transport and hydrodynamic simulations, a valley in the 2-dimensional jet-hadron correlation in azimuthal angle and rapidity arises on the top of the multiple parton interaction ridge as an unambiguous signal of the diffusion wake induced by $\gamma$-jets in heavy-ion collisions. In dijet events with a finite rapidity gap, the rapidity asymmetry of the jet-hadron correlation has been shown to be a robust signal of the diffusion wake. The same rapidity asymmetry can also be applied to $\gamma$-jet events and both are background free. Experimental measurements of these signals can provide valuable insights into the properties of the quark-gluon plasma (QGP) formed in high-energy heavy-ion collisions.

hep-ph↗

arXiv2025

A background-free signal of jet-induced diffusion wake in quark-gluon plasma

Rapidity asymmetry of jet-hadron correlation has been proposed as a robust signal of dijet-induced diffusion wake in quark-gluon plasma in high-energy heavy-ion collisions. We generalize this observable to other jet configurations such as {\gamma}/Z0-jets and propose a new method to compute the rapidity asymmetry that is free of background. In this new method, the rapidity of the trigger is fixed or restricted to a symmetrical range while the rapidity of the associated jet is varied. The rapidity asymmetry is defined as the difference between the hadron rapidity distribution in the opposite azimuthal direction of the jet (or the same direction of the trigger) with different associated jet rapidities. Since the background in this hadron rapidity distribution with different associated jet rapidity is identical, it will be completely canceled in the rapidity asymmetry. Such background-free rapidity asymmetry caused by jet-induced diffusion wake is demonstrated in CoLBT-hydro simulations of dijets and {\gamma}-jets in Pb+Pb collisions at the LHC, which weakens as hadron pT increases, and subtraction of a p+p baseline has a negligible effect.

hep-ph↗

arXiv2025

A Comprehensive Survey on Underwater Acoustic Target Positioning and Tracking: Progress, Challenges, and Perspectives

Underwater target tracking technology plays a pivotal role in marine resource exploration, environmental monitoring, and national defense security. Given that acoustic waves represent an effective medium for long-distance transmission in aquatic environments, underwater acoustic target tracking has become a prominent research area of underwater communications and networking. Existing literature reviews often offer a narrow perspective or inadequately address the paradigm shifts driven by emerging technologies like deep learning and reinforcement learning. To address these gaps, this work presents a systematic survey of this field and introduces an innovative multidimensional taxonomy framework based on target scale, sensor perception modes, and sensor collaboration patterns. Within this framework, we comprehensively survey the literature (more than 180 publications) over the period 2016-2025, spanning from the theoretical foundations to diverse algorithmic approaches in underwater acoustic target tracking. Particularly, we emphasize the transformative potential and recent advancements of machine learning techniques, including deep learning and reinforcement learning, in enhancing the performance and adaptability of underwater tracking systems. Finally, this survey concludes by identifying key challenges in the field and proposing future avenues based on emerging technologies such as federated learning, blockchain, embodied intelligence, and large models.

eess.SP↗

arXiv2025

Flavor dependence of Energy-energy correlators

Energy-energy correlators (EECs) within high energy jets serve as a key experimentally accessible quantity to probe the scale and structure of the quark-gluon plasma (QGP) in relativistic heavy-ion collisions. The CMS Collaboration's first measurement of the modification to the EEC within single inclusive jets in Pb+Pb collisions relative to p+p collisions reveals a significant enhancement at small angles, which may arise from jet transverse momentum $p_T$ selection biases due to jet energy loss. We investigate the dependence of jet EECs on the flavor of the initiating parton. The EEC distribution of a gluon jet is broader and the peak of transition from perturbative to non-perturbative regime occurs at a larger angle than a quark jet. Such flavor dependence leads to the different EECs for $\gamma$-jets and single inclusive jets due to their different flavor composition. It is also responsible for a colliding energy dependence of EECs of single inclusive jets at fixed jet energy. We also investigate the impact of flavor composition variation on the $p_T$ dependence of the jet EEC. We further propose that a change in the gluon jet fraction in A+A collisions compared to p+p can also contribute to a non-negligible enhancement of the medium modified EEC at small angles. Using the \textsc{Jewel} model, we predict the reduction of the gluon jet fraction in A+A collisions and estimate its impact on the EEC.

hep-ph↗

arXiv2025

Rapidity asymmetry of jet-hadron correlation as a robust signal of diffusion wake induced by di-jets in high-energy heavy-ion collisions

Diffusion wake accompanying a Mach cone is a unique feature of the medium response to projectiles traveling at a speed faster than the velocity of sound. This is also the case for jet-medium interaction inside the quark-gluon plasma in high-energy heavy-ion collisions. It leads to a depletion of soft hadrons in the opposite direction of the propagating jet and has been recently observed in $Z$-jet events of Pb+Pb collisions at LHC. In di-jet events, however, the diffusion wake of one jet usually overlaps with the medium-induced hadron enhancement of other jet without a clear signal except a reduction of the hadron enhancement, unless there is a large rapidity gap between the two jets. We propose to use the rapidity asymmetry of jet-hadron correlations in di-jets with a finite rapidity gap relative to that without, as a robust and background-free signal of the diffusion wake. The asymmetry emerges because the diffusion wake of one jet is shifted to a finite rapidity relative to the other jet. Consequently, a depletion of soft hadrons appears in the shifted rapidity region of the diffusion wake and an enhancement in the rapidity region of the other jet whose soft hadron enhancement is no longer or less reduced by the diffusion wake as in di-jets without a rapidity gap. We predict the rapidity asymmetry using both theoretical and mixed-event background subtraction for different values of the rapidity gap within the CoLBT-hydro model. Future measurements of this rapidity asymmetry with high statistics data on di-jets should provide more precise insights into the jet-induced diffusion wake and properties of the quark-gluon plasma.

hep-ph↗

arXiv2024

Jet-Induced Enhancement of Deuteron Production in $pp$ and $p$-Pb Collisions at the LHC

Jet-associated deuteron production in $pp$ collisions at $\sqrt{s}=13$ TeV and $p$-Pb collisions at $\sqrt{s_{NN}}=5.02$ TeV is studied in the coalescence model by using the phase-space information of proton and neutron pairs from a multiphase transport (AMPT) model at the kinetic freezeout. In the low transverse momentum ($p_T$) region $p_T/A < 1.5$ GeV/$c$, where $A$ is the mass number of a nucleus, the in-jet coalescence factor $B_2^\text{In-jet}$ for deuteron production, given by the ratio of the in-jet deuteron number to the square of the in-jet proton number, is found to be larger than the coalescence factor $B_2$ in the medium perpendicular to the jet by a factor of about 10 in $pp$ collisions and of 25 in $p-$Pb collisions, which are consistent with the ALICE measurements at the LHC. Such large low-momentum enhancements mainly come from coalescence of nucleons inside the jet with the medium nucleons. Coalescence of nucleons inside the jet dominates deuteron production only at the higher $p_T$ region of $p_T/A\gtrsim 4$ GeV/$c$, where both the yield ratio $d/p$ of deuteron to proton numbers and the $B_2$ are also significantly larger in the jet direction than in the direction perpendicular to the jet due to the strong collinear correlation among particles produced from jet fragmentation. Studying jet-associated deuteron production in relativistic nuclear collisions thus opens up a new window to probe the phase-space structure of nucleons inside jets.

nucl-th↗

arXiv2024

Probing the Short-Distance Structure of the Quark-Gluon Plasma with Energy Correlators

Energy-energy-correlators (EEC's) are a promising observable to study the dynamics of jet evolution in the quark-gluon plasma (QGP) through its imprint on angular scales in the energy flux of final-state particles. We carry out the first complete calculation of EEC's using realistic simulations of high-energy heavy-ion collisions, and dissect the different dynamics underlying the final distribution through analyses of jet propagation in a uniform medium. The EEC's of $γ$-jets in heavy-ion collisions are found to be enhanced by the medium response from elastic scatterings instead of induced gluon radiation at large angles. In the meantime, EEC's are suppressed at small angles due to energy loss and transverse momentum broadening of jet shower partons. These modifications are further shown to be sensitive to the angular scale of the in-medium interaction, as characterized by the Debye screening mass. Experimental verification and measurement of such modifications will shed light on this scale, and the short-distance structure of the QGP in heavy-ion collisions.

hep-ph↗

arXiv2024

Predictions for the sPHENIX physics program

sPHENIX is a next-generation detector experiment at the Relativistic Heavy Ion Collider, designed for a broad set of jet and heavy-flavor probes of the Quark-Gluon Plasma created in heavy ion collisions. In anticipation of the commissioning and first data-taking of the detector in 2023, a RIKEN-BNL Research Center (RBRC) workshop was organized to collect theoretical input and identify compelling aspects of the physics program. This paper compiles theoretical predictions from the workshop participants for jet quenching, heavy flavor and quarkonia, cold QCD, and bulk physics measurements at sPHENIX.

nucl-ex↗

arXiv2023

3D structure of jet-induced diffusion wake in an expanding quark-gluon plasma

The diffusion wake accompanying the jet-induced Mach cone provides a unique probe of the properties of quark-gluon plasma in high-energy heavy-ion collisions. It can be characterized by a depletion of soft hadrons in the opposite direction of the propagating jet. We explore the 3D structure of the diffusion wake induced by $γ$-triggered jets in Pb+Pb collisions at the LHC energy within the coupled linear Boltzmann transport and hydro model. We identify a valley structure caused by the diffusion wake on top of a ridge from the initial multiple parton interaction (MPI) in jet-hadron correlation as a function of rapidity and azimuthal angle. This leads to a double-peak structure in the rapidity distribution of soft hadrons in the opposite direction of the jets as an unambiguous signal of the diffusion wake. Using a two-Gaussian fit, we extract the diffusion wake and MPI contributions to the double peak. The diffusion wake valley is found to deepen with the jet energy loss as characterized by the $γ$-jet asymmetry. Its sensitivity to the equation of state and shear viscosity is also studied.

hep-ph↗

arXiv2022

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.

nucl-th↗