Session D03: Hot QCD Matter

Studying the Quark-Gluon Plasma Using Heavy-ion Collisions

The Quark-Gluon Plasma (QGP) is a hot, dense state of matter in which quarks and gluons, usually confined within protons and neutrons, become deconfined. The universe is theorized to have been a QGP shortly after the Big Bang. This state can also be produced in high energy collisions of nuclei at facilities like the Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC) in order to study its properties and improve our understanding of the strong force, governed by quantum chromodynamics, at extreme temperatures and densities. This talk will present an overview of recent results from heavy-ion collisions at the LHC and RHIC as well as prospects for future measurements including projections for sPHENIX which will start collecting data at RHIC in 2023.   

Primordial plasmas and how to study them

Relativistic heavy ion collisions create a high temperature, dense and de-confined plasma of primordial matter (quarks and gluons). This Quark-Gluon Plasma (QGP) is theorized to have existed a few micro-seconds after the big bang and thus, studying its spatio-temporal properties from its creation, evolution and eventual demise into hadronic bound matter is crucial to our understand of the early universe and interaction between fundamental particles. In this talk, I will go through an experimentalist's approach towards categorizing various properties of the QGP, and how we can measure them in experiments at both RHIC and LHC. We will end by looking forward to data from the upcoming runs at RHIC, the new sPHENIX detector and provide a long term outlook to discovery QCD physics at the EIC.   

Searching for J/psi collective flow in pp collisions at the LHC

Quark gluon plasma (QGP) is an extremely hot, dense state of matter in which the quarks and gluons, normally confined to colorless hadrons, are temporarily deconfined. The QGP can be formed in high energy nuclear collisions such as PbPb, and possibly in smaller systems such as pPb collisions. The question of how small a system can form a droplet of QGP is one of much experimental and theoretical interest. One way to probe the QGP is by measuring anisotropic flow, which manifests in long-range correlations between particles as the QGP droplet expands with anisotropic pressure gradients. It is expected that heavier particles will have little or no flow. The CMS collaboration at CERN has measured the flow of open and closed charm mesons and compared the values to those of lighter hadrons. They found the flow of J/psi mesons to be similar to that of D0 mesons in both high multiplicity pPb and PbPb collisions. While the open heavy flavor mesons may acquire flow because they contain light quarks that equilibrate in the medium, for J/psi to flow the charm quarks need to be equilibrated too, which is more difficult, especially in small systems. These observations may signal that mechanisms other than QGP formation and pressure gradients are responsible for the azimuthal anisotropy in J/psi production. To provide insight into this puzzle I aim to measure the J/psi azimuthal distributions and look for flow in pp collisions using the CMS detector. This work is in the beginning stages, so I plan to present the motivation for the measurement and performance plots demonstrating the CMS detector's ability to measure dimuon candidates for J/psi in pp collisions and their azimuthal anisotropy.   

Identifying quenching effect in heavy-ion collisions with machine learning

Quantum chromodynamics (QCD), the theory of the strong interactions, predicts a deconfined state of quarks and gluons, known as quark gluon plasma (QGP) at high temperature and/or density. This extremely hot and dense matter, believed to exist in the early universe, can be recreated in heavy ion collisions at particle accelerators such as RHIC and LHC. One of the key signatures of QGP formation is the quenching of jets, which are collimated sprays of hadrons, due to their interaction with QGP leading to parton energy loss and jet substructure modifications. While these modifications have been observed experimentally with a variety of methods averaged over many collision events, the jet-by-jet identification of quenched jets remains difficult.

We designed a machine learning approach to identify quenched jets based on their substructure. The jet showering processes are simulated with a jet quenching model Jewel and a non-quenching model Pythia 8. Sequential substructure variables are extracted from the jet clustering history following an angular-ordered sequence and are used in the training of a neural network built on top of a long short-term memory network. We show that this approach successfully identifies the quenching effect in the presence of the large uncorrelated background of soft particles created in heavy ion collisions.