Related papers: A deep learning approach for predicting multiple o…
Particle identification in large high-energy physics experiments typically relies on classifiers obtained by combining many experimental observables. Predicting the probability density function (pdf) of such classifiers in the multivariate…
We present a simple way to construct 3D initial conditions for relativistic heavy-ion collisions based on the Glauber collision geometry. Local energy and momentum conservation conditions are imposed to set non-trivial constraints on our…
Despite their importance in a wide variety of applications, the estimation of ionization cross sections for large molecules continues to present challenges for both experiment and theory. Machine learning algorithms have been shown to be an…
In this dissertation I introduce relativistic heavy ion collisions and describe theoretical approaches to understanding them--in particular, viscous hydrodynamic simulations and investigations of final state interactions. The successful…
Recent experimental results obtained in STAR experiment at the Relativistic heavy-ion collider (RHIC) with ion beams will be discussed. Investigations of different nuclear collisions in some recent years focus on two main tasks, namely,…
Posterior distributions for physical parameters describing relativistic heavy-ion collisions, such as the viscosity of the quark-gluon plasma, are extracted through a comparison of hydrodynamic-based transport models to experimental results…
A deep learning based method with the convolutional neural network (CNN) algorithm for determining the impact parameters is developed using the constrained molecular dynamics model simulations, focusing on the heavy-ion collisions at the…
PHENIX measurements of high transverse momentum ($p_T$) identified hadrons in $d$+Au and Au+Au collisions are presented. The nuclear modification factors ($R_{d{\rm A}}$ and $R_{\rm AA}$) for $\pi^0$ and $\eta$ are found to be very…
ANN model is used to estimate the multiplicity per rapidity for charged pions and kaons observed in various high-energy experiments from central Au+Au collisions with energies ranging from 2-200 GeV, and then compared to available…
We report on measurements of directed flow as a function of pseudorapidity in Au+Au collisions at energies of $\sqrt{s_{_{NN}}} =$ 19.6, 62.4, 130 and 200 GeV as measured by the PHOBOS detector at the Relativistic Heavy Ion Collider (RHIC).…
Measurements of various particle species over an extended momentum range provide a sensitive experimental tool for investigating particle production mechanisms in hadronic collisions. Comparison of the spectral shapes from different…
The experiments at the Large Hadron Collider at CERN generate vast amounts of complex data from high-energy particle collisions. This data presents significant challenges due to its volume and complex reconstruction, necessitating the use…
Light nuclei and hyper-nuclei are produced in abundance in heavy-ion collisions. The production mechanism of these species in heavy-ion collisions still remains to be understood. In these proceedings, we report the transverse momentum and…
Physical motion models offer interpretable predictions for the motion of vehicles. However, some model parameters, such as those related to aero- and hydrodynamics, are expensive to measure and are often only roughly approximated reducing…
We calculate initial conditions for the hydrodynamical evolution in ultrarelativistic heavy-ion collisions at the LHC and RHIC in an improved next-to-leading order perturbative QCD + saturation framework. Using viscous relativistic…
This paper describes the conclusions that can be drawn from the data taken thus far with the PHOBOS detector at RHIC. In the most central Au+Au collisions at the highest beam energy, evidence is found for the formation of a very high energy…
We present various order moments (Variance ($\sigma^2$), Skewness($S$), Kurtosis($\kappa$)) of net-proton multiplicity distributions in Au+Au collisions from the first phase of RHIC beam energy scan ({\sNN}=7.7$-$200 GeV). The measurements…
Using deep convolutional neural network (CNN), the nature of the QCD transition can be identified from the final-state pion spectra from hybrid model simulations of heavy-ion collisions that combines a viscous hydrodynamic model with a…
Neural networks have emerged as a powerful way to approach many practical problems in quantum physics. In this work, we illustrate the power of deep learning to predict the dynamics of a quantum many-body system, where the training is…
Heavy ion collisions at RHIC energies (Au+Au collisions at $\sqrt{s_{NN}}=200$ GeV) exhibit significant new features as compared to earlier experiments at lower energies. The reaction is characterized by a high degree of transparency of the…