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researcher

Gang Wang

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • nucl-th3
  • physics.data-an1
same name
  • Gang Wang — 19 papers, h 8
  • Gang Wang — 12 papers, h 19
  • Gang Wang — 11 papers, h 46
  • Gang Wang — 11 papers, h 19
  • Gang Wang — 11 papers, h 8
  • Gang Wang — 9 papers, h 20

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators

4 papers

nucl-th2026

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

Rushil Saraswat, Aditya Prasad Dash, Huan Zhong Huang +3

Energy-energy correlators (EECs) provide a sensitive probe of both perturbative and nonperturbative dynamics in relativistic heavy-ion collisions. Jet-medium interactions enhance p…

nucl-th2024

Methodology for Analyzing Proton Multiplicity Fluctuations with Azimuthal Partitions in Heavy-Ion Collisions

Dylan Neff, Zhongling Ji, Roli Esha +2

A primary objective in high-energy heavy-ion collisions is to investigate the phase transition between confined and deconfined color matter. Complementary to the cumulants of conse…

nucl-th2024

Impact of Limited Statistics on the Measured Hyper-Order Cumulants of Net-Proton Distributions in Heavy-Ion Collisions

Lizhu Chen, Ye-Yin Zhao, Yunshan Cheng +3

Hyper-order cumulants C5​/C1​ and C6​/C2​ of net-baryon distributions are anticipated to offer crucial insights into the phase transition from quark-gluon plasma to hadronic ma…

physics.data-an2024

Mini-jet Clustering Algorithm Using Transverse-momentum Seeds in High-energy Nuclear Collisions

Hanpu Jiang, Nanxi Yao, Cheuk-Yin Wong +2

We propose an algorithm to detect mini-jet clusters in high-energy nuclear collisions, by selecting a high-transverse-momentum (pT​) particle as a seed and assigning a clustering…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.