From the 1 of 4 linked papers with an AI index.
4 papers
Unbiased Data-Driven Determination of the Nuclear Dipole Amplitude in the Color Glass Condensate
Si-Wei Dai, Haowu Duan, Long-Gang Pang +4
The paper presents a physics‑informed neural‑network method that embeds the Balitsky‑Kovchegov evolution to extract the nuclear gluon dipole amplitude directly from data, yielding…
Jet cone size dependence of single inclusive jet suppression due to jet quenching in Pb+Pb collisions at TeV
Qing-Fei Han, Man Xie, Han-Zhong Zhang
Jet suppression in high-energy heavy-ion collisions results from jet energy loss and transverse-momentum broadening during jet propagation through the quark-gluon plasma (QGP). The…
Parton Fragmentation Functions Extracted with a Physics-Informed Neural Network
Si-Wei Dai, Fu-Peng Li, Long-Gang Pang +3
Reliable predictions of many high-energy strong interaction processes rely heavily on the non-perturbative parton fragmentation functions (FFs) extracted from existing experimental…
Extracting Essential Non-perturbative Information in Jet Invariant Mass via the Bayesian Analysis
Zhan Gao, Yu Shi, Bo-Wen Xiao +1
In this paper, we present a new three-dimensional non-perturbative (NP) function to account for and parameterize the NP contributions in the jet invariant mass spectrum, in additio…