Search Results for author:
Found 5 papers, 1 papers with code
Machine Learning in Nuclear Physics
Advances in machine learning methods provide tools that have broad applicability in scientific research.
Search for the elusive jet-induced diffusion wake in $Z/γ$-jets with 2D jet tomography in high-energy heavy-ion collisions
Diffusion wake is an unambiguous part of the jet-induced medium response in high-energy heavy-ion collisions that leads to a depletion of soft hadrons in the opposite direction of the jet propagation.
High Energy Physics - Phenomenology
An equation-of-state-meter of quantum chromodynamics transition from deep learning
A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions.
An equation-of-state-meter of QCD transition from deep learning
Supervised learning with a deep convolutional neural network is used to identify the QCD equation of state (EoS) employed in relativistic hydrodynamic simulations of heavy-ion collisions from the simulated final-state particle spectra $\rho(p_T,\Phi)$.
HIJING 1.0: A Monte Carlo Program for Parton and Particle Production in High Energy Hadronic and Nuclear Collisions
Based on QCD-inspired models for multiple jets production, we developed a Monte Carlo program to study jet and the associated particle production in high energy $pp$, $pA$ and $AA$ collisions.
Nuclear Theory High Energy Physics - Phenomenology
