activity
20222026
most citedNumber-of-constituent-quark scaling of elliptic flow: a quantitative study

28 citations · 28 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Measuring Behavioural Signatures of Large Language Models through Psychometric Profiling

Yu Sha, Junqi Tao, Dixin Zhou +10

Large language models (LLMs) increasingly mediate human decisions and communication, yet their behavioural regularities remain difficult to characterize systematically. We develop…

nucl-th2026

Deep learning approaches to extract nuclear deformation parameters from initial-state information in heavy-ion collisions

Jun-Qi Tao, Yang Liu, Yu Sha +5

The deformation of heavy nuclei leaves characteristic imprints on the initial conditions of relativistic heavy-ion collisions. However, event-by-event fluctuations make the quantit…

nucl-th2025

Initial spin fluctuations as a probe of cluster spin structure in and nuclei

Xiang Fan, Jun-Qi Tao, Ze-Fang Jiang +1

We investigate the imprint of clustering on initial spin fluctuations in relativistic and collisions a…

nucl-th2025

A deep learning approach for predicting multiple observables in Au+Au collisions at RHIC

Jun-Qi Tao, Xiang Fan, Yang Liu +4

We develop a neural network model, based on the processes of high-energy heavy-ion collisions, to study and predict several experimental observables in Au+Au collisions. We present…

hep-ph2022★ 28 cited

Number-of-constituent-quark scaling of elliptic flow: a quantitative study

M. Wang, J. Q. Tao, H. Zheng +3

The number-of-constituent-quark (NCQ) scaling behavior of the elliptic flow of identified particles produced in A+A collisions is studied quantitatively using an empirical function…