摘要：
Measurements of the semi-inclusive distribution of charged jets recoiling from a trigger hadron in pp and Pb-Pb collisions at root s(NN) = 5.02 TeV are presented. This technique provides precise, data-driven subtraction of the large uncorrelated background in jet measurements. It uniquely enables the exploration of medium-induced modification of jet production and acoplanarity over wide phase space, including low transverse momentum (p(T)) and large resolution parameter (R) jets. This proceeding reports the measurements of medium-induced jet energy redistribution through the comparison of trigger-normalized recoil jet yields in pp and Pb-Pb collisions, and for jets with different R.
作者机构：
[Pang, Long-Gang] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;[Pang, Long-Gang] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.
通讯机构：
[Pang, Long-Gang] C;Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.
会议名称：
28th International Conference on Ultra-Relativistic Nucleus-Nucleus Collisions (Quark Matter)
会议时间：
NOV 04-09, 2019
会议地点：
Wuhan, PEOPLES R CHINA
会议主办单位：
[Pang, Long-Gang] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.^[Pang, Long-Gang] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.
关键词：
Heavy ion collisions;deep learning;machine learning for physics
摘要：
The high energy heavy ion collision is a multi-stage process that is described by complex hybrid models. The initial state fluctuations in event-by-event simulations of heavy ion collisions convert to final state correlations by collective flow and hadronic cascade. It is not easy to design final state correlations (observables) from particles in momentum space, that can help to extract useful information, such as the initial state nuclear structure, the properties of quark gluon plasma and the nuclear equation of state. Machine learning is helpful in automatic feature extraction in heavy ion collisions. This article reviews the applications, challenges and possible future developments of machine learning in heavy ion physics.
关键词：
Heavy-ion physics;QCD equation of state;Hybrid model;Deep learning
摘要：
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 collisions. Within this hybrid model, a viscous hydrodynamic model is coupled with a hadronic cascade “after-burner”. As a binary classification setup, we employ two different types of equations of state (EoS) of the hot medium in the hydrodynamic evolution. The resulting final-state pion spectra in the transverse momentum and azimuthal angle plane are fed to the neural network as the input data in order to distinguish different EoS. To probe the effects of the fluctuations in the event-by-event spectra, we explore different scenarios for the input data and make a comparison in a systematic way. We observe a clear hierarchy in the predictive power when the network is fed with the event-by-event, cascade-coarse-grained and event-fine-averaged spectra. The carefully-trained neural network can extract high-level features from pion spectra to identify the nature of the QCD transition in a realistic simulation scenario.
作者机构：
[Ashraf, Muhammad Usman] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;[Ashraf, Muhammad Usman] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;[STAR Collaboration; Ashraf, Muhammad Usman] Tsinghua Univ, Dept Engn Phys, Beijing 100084, Peoples R China.
通讯机构：
[Ashraf, Muhammad Usman] C;[Ashraf, Muhammad Usman] T;Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;Tsinghua Univ, Dept Engn Phys, Beijing 100084, Peoples R China.
会议名称：
28th International Conference on Ultrarelativistic Nucleus-Nucleus Collisions (Quark Matter)
会议时间：
NOV 04-09, 2019
会议地点：
Wuhan, PEOPLES R CHINA
会议主办单位：
[Ashraf, Muhammad Usman] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.^[Ashraf, Muhammad Usman] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.^[Ashraf, Muhammad Usman;STAR Collaboration] Tsinghua Univ, Dept Engn Phys, Beijing 100084, Peoples R China.
摘要：
We report recent results of strangeness production in Au+Au collisions at root s(NN) = 54.4 GeV and Al+Au fixed-target collisions at root s(NN) = 4.9 GeV from the STAR experiment at RHIC. The collision-energy dependence of strange hadron yields is presented. The p(T) dependance of nuclear modification factor (R-CP) and baryon to meson ratio ((Lambda) over bar /K-S(0) are measured to study the recombination and parton energy loss mechanisms. The STAR fixed-target data are compared to the previous published data from different AGS experiments. The physics implications for collision dynamics are discussed.
