The document summarizes a Monte Carlo investigation of the Quark Gluon Plasma using the JEWEL generator. Jet asymmetry (AJ) and the ratio of 3-jet events to 2-jet events (R32) were considered for varying collision centralities and compared to pp collisions. The results showed that AJ behaved as expected and that R32 decreases with increasing centrality, indicating it may be useful for studying the Quark Gluon Plasma. Further refinement of the models is needed to better match the R32 values to experimental data.

1. Monte Carlo Investigation of Quark Gluon Plasma
Ian Hunt-Isaak
Oberlin College
Abstract
The study of the Quark Gluon Plasma (QGP) is greatly facilitated by numerical methods such as Monte Carlo(MC) Simulations. In this work an
interface with established MC generators was improved upon and used to investigate PbPb collisions. Using the JEWEL MC generator Jet Asymmetry, AJ,
and the 3 to 2 jet ratio, R32, were considered while varying the centrality of collisions. By comparing conditions of varying centrality with
simulated pp collisions it was determined that the models were behaving as expected. It was also found that R32 shows promise as a jet observable for investigation of the QGP.
Introduction
• QGP The Quark-Gluon Plasma is the extremely hot and dense state of deconfined Quarks and Gluons
generated by the collision of Heavy Ions at Ultra relativistic velocities. The QGP is theorized to be similar
to what existed in the very early universe, when generating it we create a "little big bang". As can be seen
in Fig. 1 it is very short lived so internal probes such as Jets are necessary to probe the QGP medium’s
properties.
Figure 1 : A cartoon depicting the time evolution of the QGP [1]
• Jets Jets are the result of a hard interaction between the components of the colliding nuclei. A high
momentum quark or gluon is ejected from the collision center, speeds off and fragments into particles
whose energies are deposited in the detector. The Anti-kt algorithm [5] is then used to group energy
deposits to form the notion of a jet.
Figure 2 : Depiction of a Particle Jet’s origin and detection [2]
• Jet Quenching Jets can lose energy through strong interactions with the medium in what is known as
Jet Quenching. This is one of the signature distinguishing features between PbPb and pp collisions. It is
through understanding Jet interactions with the medium such as quenching that we can understand the
medium generated by PbPb collisions.
Figure 3 : Left: pp Collision Dijet event. Right: PbPb Collision Dijet Event. One Jet has lost momentum
due to interaction with the medium, this is Jet Quenching. [3]
• Centrality Centrality is a measure how a head on a collision is. It runs from 0% for direct collisions to
100% for the most peripheral collisions. Centrality can be determined by summing the total energy
deposits in the forward calorimeters as can be seen in Fig. 4
Figure 4 : Energy Deposits and Corresponding Centrality Range [4]
Less central collisions 80-90% are expected to be more similar to proton proton collisions than more
central (0-10%) collisions as more peripheral collisions should generate less QGP than direct collisions.
Analysis
Collision events can be simulated via random sampling of a
probability distribution describing the physics in what is is known as a Monte Carlo Method. By varying param-
eters in our simulations of the medium and seeing what most accurately describes the data we can learn about
the Physics of the medium.
There are many multiple MC Generators freely available including
PYTHIA [6], JEWEL [7], PYQUEN [8] and Q-Pythia [9].
PYTHIA is the standard MC generator for pp collisions, the other
Generators listed are based on PYTHIA and simulate PbPb collisions
and include interactions with the medium. This summer I set up a
framework to run the above Generators available in an efficient and
easy way, this is available at github.com/ianhi/GeneratorInterface/ . I
then focused on using JEWEL as it has the advantage of an excellent list of medium parameters in the
directions for use [7].
Jet Observables
AJ Jet Asymmetry serves as a measure of jet quenching by comparing the transverse momenta of the two highest
momentum jets in an event. Defined as
AJ =
PT1 − PT2
PT1 + PT2
we can use it to determine if our model is simulating more peripheral collisions are more pp like than the more
central collisions as we expect. Thus AJ serves as a check on our models.
R32, the Three-Jet to Two-Jet ratio, is the ratio of the number of events containing three jets to the number
containing two jets. It is defined as
R32 =
dσ3/HT
dσ2/HT
where HT is the total jet transverse momentum of the event [10]. One would expect that this quantity will
be reduced in PbPb compared to pp due the loss of Jets to strong quenching and increased background. We
may also see the reduction countered by a possible creation of more jets due to the greater number of hard
interactions in PbPb collisions. While this has been investigated for proton-proton collisions [10] it has yet to
be considered for PbPb collisions.
Results
Jewel was used to compare the effects of different centralities on R32 to investigate if this quantity is affected
by Jet Interactions with the medium. From the comparison of AJ for the JEWEL and PYTHIA simulations
we find that Jewel is not only modeling quenching but that more Peripheral Collisions are more similar to pp
collisions. The AJ plot gives us confidence that our models are behaving as expected.
In the plot of the R32 we see clearly that interactions with the medium have reduced the value of R32 and we
see the expected centrality dependence. There is room for improvement on these plots through varying the jet
definition. Different PT cuts and clustering radii have given somewhat different shapes for R32, in particular the
ratio for pp PYTHIA events flattens at increasing HT and remains below unity with a higher PT cut.
Figure 5 : Left: Plot AJ demonstrating that Jet Quenching was modeling and showing a centrality dependence.
Right: The 3-Jet/2-Jet Ratio for Pythia and Jewel at multiple centralities.
Next Steps
These initial results imply that R32 may be useful to investigate the properties of the QGP. The next steps are
to calculate R32 for PbPb data and then to adjust the model parameters so the MC simulations match the data.
Acknowledgements
This work was done at the Rutgers University REU program under Raghav Kunnawalkam Elayavalli, and Professor
Sevil Salur. It was funded by NSF Grant PHY-126328.
References
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