185 citations · 1.3k across the 49 of their papers we have counts for
49 papers
Phase Transition Study meets Machine Learning
Yu-Gang Ma, Long-Gang Pang, Rui Wang +1
In recent years, machine learning (ML) techniques have emerged as powerful tools for studying many-body complex systems, and encompassing phase transitions in various domains of ph…
Mass and tidal parameter extraction from gravitational waves of binary neutron stars mergers using deep learning
Shriya Soma, Horst Stöcker, Kai Zhou
Gravitational Waves (GWs) from coalescing binaries carry crucial information about their component sources, like mass, spin and tidal effects. This implies that the analysis of GW…
High energy nuclear physics meets Machine Learning
Wan-Bing He, Yu-Gang Ma, Long-Gang Pang +2
Though being seemingly disparate and with relatively new intersection, high energy nuclear physics and machine learning have already begun to merge and yield interesting results du…
Model dependence of the number of participant nucleons and observable consequences in heavy-ion collisions
Manjunath Omana Kuttan, Jan Steinheimer, Kai Zhou +2
The centrality determination and the estimated fluctuations of number of participant nucleons in Au-Au collisions at 1.23 GeV beam kinetic energy suffers from severe…
Bottom energy loss and non-prompt production in relativistic heavy ion collisions
Meimei Yang, Shiqi Zheng, Bo Tong +4
We study the momentum and centrality dependence of the non-prompt nuclear modification factors (), which comes from the hadrons decay, in Pb-Pb collisions at the…
Examination of nucleon distribution with Bayesian imaging for isobar collisions
Yi-Lin Cheng, Shuzhe Shi, Yu-Gang Ma +2
Relativistic collision of isobaric systems is found to be valuable in differentiating the nucleon distributions for nuclei with the same mass number. In recent contrast experiment…
QCD Equation of State of Dense Nuclear Matter from a Bayesian Analysis of Heavy-Ion Collision Data
Manjunath Omana Kuttan, Jan Steinheimer, Kai Zhou +1
Bayesian methods are used to constrain the density dependence of the QCD Equation of State (EoS) for dense nuclear matter using the data of mean transverse kinetic energy and ellip…
Continuum-extrapolated NNLO Valence PDF of Pion at the Physical Point
Xiang Gao, Andrew D. Hanlon, Nikhil Karthik +7
We present lattice QCD calculations of valence parton distribution function (PDF) of pion employing next-to-next-leading-order (NNLO) perturbative QCD matching. Our calculations ar…
Rethinking the ill-posedness of the spectral function reconstruction -- why is it fundamentally hard and how Artificial Neural Networks can help
Shuzhe Shi, Lingxiao Wang, Kai Zhou
Reconstructing hadron spectral functions through Euclidean correlation functions are of the important missions in lattice QCD calculations. However, in a Källen--Lehmann(KL) spectr…
Neural network reconstruction of the dense matter equation of state from neutron star observables
Shriya Soma, Lingxiao Wang, Shuzhe Shi +2
The Equation of State (EoS) of strongly interacting cold and hot ultra-dense QCD matter remains a major challenge in the field of nuclear astrophysics. With the advancements in mea…
Learning Langevin dynamics with QCD phase transition
Lingxiao Wang, Lijia Jiang, Kai Zhou
In this proceeding, the deep Convolutional Neural Networks (CNNs) are deployed to recognize the order of QCD phase transition and predict the dynamical parameters in Langevin proce…
Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning
Yongjia Wang, Fupeng Li, Qingfeng Li +2
A deep convolutional neural network (CNN) is developed to study symmetry energy effects by learning the mapping between the symmetry energy and the two-dimensional…
An equation-of-state-meter for CBM using PointNet
Manjunath Omana Kuttan, Kai Zhou, Jan Steinheimer +2
A novel method for identifying the nature of QCD transitions in heavy-ion collision experiments is introduced. PointNet based Deep Learning (DL) models are developed to classify th…
Shared Data and Algorithms for Deep Learning in Fundamental Physics
Lisa Benato, Erik Buhmann, Martin Erdmann +10
We introduce a Python package that provides simply and unified access to a collection of datasets from fundamental physics research - including particle physics, astroparticle phys…
Detecting Chiral Magnetic Effect via Deep Learning
Yuan-Sheng Zhao, Lingxiao Wang, Kai Zhou +1
The search of chiral magnetic effect (CME) in heavy-ion collisions has attracted long-term attentions. Multiple observables have been proposed but all suffer from obstacles due to…
Machine learning based approach to fluid dynamics
Kirill Taradiy, Kai Zhou, Jan Steinheimer +3
We study the applicability of a Deep Neural Network (DNN) approach to simulate one-dimensional non-relativistic fluid dynamics. Numerical fluid dynamical calculations are used to g…
Heavy quark potential in quark-gluon Plasma: Deep neural network meets lattice quantum chromodynamics
Shuzhe Shi, Kai Zhou, Jiaxing Zhao +2
Bottomonium states are key probes for experimental studies of the quark-gluon plasma (QGP) created in high-energy nuclear collisions. Theoretical models of bottomonium productions…
Deep learning stochastic processes with QCD phase transition
Lijia Jiang, Lingxiao Wang, Kai Zhou
It is non-trivial to recognize phase transitions and track dynamics inside a stochastic process because of its intrinsic stochasticity. In this paper, we employ the deep learning m…
Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk
Lingxiao Wang, Tian Xu, Till Hannes Stoecker +3
As the COVID-19 pandemic continues to ravage the world, it is of critical significance to provide a timely risk prediction of the COVID-19 in multi-level. To implement it and evalu…
A fast centrality-meter for heavy-ion collisions at the CBM experiment
Manjunath Omana Kuttan, Jan Steinheimer, Kai Zhou +2
A new method of event characterization based on Deep Learning is presented. The PointNet models can be used for fast, online event-by-event impact parameter determination at the CB…
Identifying the nature of the QCD transition in heavy-ion collisions with deep learning
Yi-Lun Du, Kai Zhou, Jan Steinheimer +5
In this proceeding, we review our recent work using deep convolutional neural network (CNN) to identify the nature of the QCD transition in a hybrid modeling of heavy-ion collision…
Unsupervised Outlier Detection in Heavy-Ion Collisions
Punnathat Thaprasop, Kai Zhou, Jan Steinheimer +1
We present different methods of unsupervised learning which can be used for outlier detection in high energy nuclear collisions. The UrQMD model is used to generate the bulk backgr…
Heavy flavors under extreme conditions in high energy nuclear collisions
Jiaxing Zhao, Kai Zhou, Shile Chen +1
Heavy flavor hadrons have long been considered as a probe of the quark gluon plasma created in high energy nuclear collisions. In this paper we review the heavy flavor properties u…
Continuous-mixture Autoregressive Networks for efficient variational calculation of many-body systems
Lingxiao Wang, Yin Jiang, Lianyi He +1
We develop deep autoregressive networks with multi channels to compute many-body systems with \emph{continuous} spin degrees of freedom directly. As a concrete example, we embed th…