Quark Physics Laboratory
Graduate School of Advanced Science and Engineering
Physics Program
Hiroshima University, Japan

Simulation Study to Identify Muons from
Heavy Flavor Mesons in Proton-Proton andLead-Lead Collisions at LHC ALICE Run 3

Master’s Thesis
in partial fulfillment of the requirements for the degree of Master of Sciences(M.Sc)

Name:
Student ID No.:
Email:
Supervisor:
Chief Examiner:
Due Date:

Muhamad Noor Izwan Bin Ishak
M214071
noor_izwan@quark.hiroshima-u.ac.jp
Prof. Kenta Shigaki
Prof. Kenta Shigaki
July 25, 2023

Abstract
The primary objective of this research is to reveal the properties of the Quark Gluon
Plasma (QGP) phase created during heavy ion collisions. The QGP is a state ofmatter that is believed to exist at extreme temperatures and densities, similar to thosethat existed shortly after the Big Bang. The study of the QGP provides insights into
the fundamental nature of matter and the evolution of the universe.
This study focuses on investigating the properties of single heavy flavor B andD meson particles in relativistic heavy ion collisions, with a particular emphasis on
their decaying process. These heavy quarks are produced through hard-scattering
processes during the early stages of heavy-ion collisions and decay into muons andelectrons. Heavy quarks, such as B and D mesons, offer critical insights into theproperties of the Quark Gluon Plasma (QGP) phase created during heavy ion collisions. To conduct more detailed studies of QGP properties, the contribution of
beauty (from B mesons) to the muon yield must be separated from that of charm(from D mesons). The measurement of charm and beauty elliptic flow will assist in
gaining a comprehensive understanding of in-medium parton energy loss and thermalization. The large production yield of heavy quarks at the LHC makes the studyof heavy flavor production in heavy-ion collisions of great interest. It is possible tomeasure both charm and beauty hadrons in similar kinematic ranges and perform
a direct comparison via the "double" nuclear modification factors. Measuring thetotal production cross section of heavy quarks in heavy ion collisions is a crucial
reference for studying the modification of quarkonia (charmonia and bottomonia)
production yield, which is directly linked to the production cross section of thecorresponding heavy quarks.
Muons and electrons can be detected by the forward muon spectrometer andthe Muon Forward Tracker (MFT) in ALICE at LHC energies. The MFT has
been incorporated into the experimental setup to improve heavy flavor measurements. The analysis includes a comparison between MFT muon simulated tracks
and Monte Carlo (MC) simulations. The incorporation of the MFT into the experimental setup enables more precise measurements of heavy flavor particles, contributing to a deeper understanding of the QGP phase.
This paper focuses on the comparison of several kinematic quantities, such as
the distance of closest approach (DCA), the transverse momentum (pT ), and scattering angles distributions of decay muons from B mesons, D mesons, kaons, andpions. Full simulations are conducted to study the particles produced in the forward
rapidity region (-3.6 < η < -2.5) with a momentum greater than 4 GeV/c in both pp
and Pb-Pb collisions. The muon tracks’ shape introduces a detector bias feature that
may affect the measurement results, and corrections are made after confirming the2correct algorithms for parameters. The assessment of DCA and pT measurements
is conducted for both linear and curved muon tracks to improve the separation analysis.
The results of this analysis provide critical information on the properties andevolution of the QGP, shedding light on the fundamental nature of matter under extreme conditions. Specifically, the research focuses on understanding the behaviorof heavy quarks in the QGP and their decay products.

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Contents
1

Introduction
1.1 Quantum Chromodynamics(QCD) . . . . . . . . . . . . . . . . . .
1.2 Quark Gluon Plasma(QGP) . . . . . . . . . . . . . . . . . . . . . .
1.3 D and B mesons in Heavy Ion Collisions . . . . . . . . . . . . . . .
1.3.1 Importance of study of B and D mesons . . . . . . . . . . .
1.3.2 Decay Process of D and B mesons . . . . . . . . . . . . . .
1.3.3 Main Decay Channel of D and B Mesons . . . . . . . . . .
1.4 Purpose of the study . . . . . . . . . . . . . . . . . . . . . . . . . .

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17

2

Simulation and Experimental Circumstances
2.1 ALICE Experiment . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Muon Forward Tracker (MFT) . . . . . . . . . . . . . . . . . . . .
2.3 Muon Spectrometer . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4 O2 (Online-Offline) Data Framework . . . . . . . . . . . . . . . .
2.5 Geant4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.6 Pythia 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.7 Track Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.8 Global Forward Track . . . . . . . . . . . . . . . . . . . . . . . . .
2.9 Monte Carlo (MC) track . . . . . . . . . . . . . . . . . . . . . . .
2.10 Transverse momentum (pT ) . . . . . . . . . . . . . . . . . . . . . .
2.11 Distance of Closest Approach (DCAT ) . . . . . . . . . . . . . . .
2.11.1 DCA calculation with cartesian coordinate . . . . . . . . .

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3

Data Analysis – Extraction of Heavy Flavour Decay Muons
3.1 Expected Decay Muon Distribution in Global Muon Track . . . . .
3.2 Expected Decay Muon Distribution in Monte Carlo Track . . . . . .
3.3 Signals and Background Muons in Monte Carlo Track . . . . . . .
3.4 Prompt decay muons from light mesons . . . . . . . . . . . . . . .

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4

Summary52

4

References

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5

List of Figures
1.1
1.2
1.3
1.4
1.5
1.6
1.7

2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8

3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8

Classification of lepton and quark types [1] . . . . . . . . . . . . .
Colour quarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Anticolor quarks . . . . . . . . . . . . . . . . . . . . . . . . . . .
Quark-antiquark pair . . . . . . . . . . . . . . . . . . . . . . . . .
QGP state in schematic phase diagram of QCD [2] . . . . . . . . .
Properties of D meson types [3] . . . . . . . . . . . . . . . . . . .
Another decay channels of B and D mesons into another mesons.
Branching ratio of both decays: B(B + → D0 l+ vl ) = 2.35 ± 0.09%
and B(D+ → K + µ+ vµ ) = 8.76 ± 0.19% [4]. . . . . . . . . . . . . .
Layout of Large Hadron Collider (LHC) . . . . . . . . . . . . . . .
Components of ALICE Run-3 [5] . . . . . . . . . . . . . . . . . .
MFT half disks . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MFT Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Layout of the muon spectrometer. The tracking system consists ofstations 1 to 5 and trigger system of stations 6 and 7 [6] . . . . . . .
Data volume reduction in O2 framework [7]. . . . . . . . . . . . . .
Track reconstruction in the Muon spectrometer and MFT . . . . . .
Distance of Closest Approach (DCA), the distance between a beam
center and a center of the circle [8]. . . . . . . . . . . . . . . . . .

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13

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Primary vertices of particles in horizontal coordinate. There is aslight spreading of primary vertices beyond the the center of collision. 33
DCAT distribution of muons for GM Track of pp. . . . . . . . . . . 34
DCAT distribution of muons for GM Track of Pb-Pb . . . . . . . . 35
The formation of near uniform distribution in xy-plane at z=0 cm. . 35
DCA2T distribution of muons for GM Track of pp . . . . . . . . . . 35
DCA2T distribution of muons for GM Track of Pb-Pb . . . . . . . . 36
DCAT distribution of muons for MC Track pp . . . . . . . . . . . . 37
DCAT distribution of muons for MC Track Pb-Pb . . . . . . . . . 38

6

DCAT distributions of decay muons in different pT ranges for MC
track of pp. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.10 DCAT distributions of decay muons in different pT ranges for MC
track of Pb-Pb. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.11 (a) Mean and (b) resolution for MC track of pp. . . . . . . . . . . .
3.12 (a) Mean and (b) resolution for MC track of Pb-Pb. . . . . . . . . .
3.13 pT distributions for signal muons and background muons for MC
track of (a) pp and (b) Pb-Pb . . . . . . . . . . . . . . . . . . . . .
3.14 (a) DCAT distributions for signal muons and background muons√
3for MC track of pp and their (b) statistical significance, BS distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.15 (a) DCAT distributions for signal muons and background muons√
3for MC track of Pb-Pb and their (b) statistical significance, BS
distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.16 pT distributions result for pp after applying cutoff only for region
DCAT < 0.03 cm . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.17 pT distributions result for Pb-Pb after applying cutoff only for region DCAT < 0.03 cm . . . . . . . . . . . . . . . . . . . . . . . .
3.18 (a) pT and (b) DCAT distributions of prompt decay muons fromlight mesons for MC track of pp. . . . . . . . . . . . . . . . . . . .
3.19 (a) pT and (b) DCAT distributions of prompt decay muons fromlight mesons for MC track of Pb-Pb. . . . . . . . . . . . . . . . . .
3.9

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7

List of Tables
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9

Simulation Set Up for pp and Pb-Pb collisions . . . . . . . . . . . .
Number of muon decay in GM Track of pp collisions . . . . . . . .
Number of muon decay in GM Track of Pb-Pb collisions . . . . . .
Number of muon decay in MC Track pp collisions . . . . . . . . . .
Number of muon decay in MC Track Pb-Pb collisions . . . . . . . .
Decay muons count in different pT ranges for MC track of pp. . . .
Decay muons count in different pT ranges for MC track of Pb-Pb. .
Parent particles and their decay muon count in pp. . . . . . . . . . .
Parent particles and their decay muon count in Pb-Pb. . . . . . . . .

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8

Chapter 1
Introduction
1.1

Quantum Chromodynamics(QCD)

There are currently four known fundamental interactions of nature: electromagnetic, strong, weak, and gravitational. The standard model of particle physics describes three of these interactions: strong, weak, and electroweak, all of which caninvolve the exchange of vector bosons. The four known vector bosons are the photon, W and Z bosons, and the gluon. They all have spin 1. The photon is responsible
for the electromagnetic force, the W and Z bosons mediate the weak force, and thegluon mediates the strong force.
Quantum Chromodynamics (QCD) is a fundamental component of the standard
model that describes the strong interaction between quarks and gluons, which are
the particles that form hadrons including protons and neutrons. This means that,
unlike the electromagnetic force, which weakens as particles separate, the strong
force between hadrons becomes stronger as the distance between particles increases.
This results in a phenomenon called confinement, where quarks are always confinedwithin hadrons and cannot exist as free particles. The energy of the colour field also
increases with increasing distance. At distances greater than 1 fm, it is sufficiently
large to produce real quark-antiquark pairs.
In 1964, Gell-Mann and Zweig postulated that physical hadrons are composite
objects made up of three quarks (baryons) or a quark-antiquark pair (mesons) [9].
These three quarks are known as the three flavors: up (u), down (d), strange (s),charm (c), bottom (b), and top (t). Gluons are the particles that mediate the strong
force between quarks and come in eight different types. The eight gluons mediate
the interaction between particles carrying color charge, including quarks, gluons
themselves, and antiparticles. This is an important difference from the electromagnetic interaction, where the photon field quanta have no charge and cannot interact
with each other. The quantum number of color can assume three values: red, blue,

9and green, and antiquarks carry the anticolors anti-red, anti-blue, and anti-green.
A single quark can only be detached from a hadron by producing at least two freeobjects carrying color: the quark and the remainder of the hadron. This implies that
only colorless particles, i.e., particles with no net color, can exist as free particles,
explaining why quarks are not observed as free particles.
The exchange of gluons is crucial for studying the properties of the Quark Gluon
Plasma (QGP), a state of matter believed to exist at extreme temperatures and densities, similar to those shortly after the Big Bang.

Figure 1.1: Classification of lepton and quark types [1]Figurequarks1.2:

ColourFigure 1.3:
quarks

Anticolor

1.2

Quark Gluon Plasma(QGP)

Figure 1.4:
antiquark pair

Quark-

Quark-gluon plasma (QGP) is a state of matter that is believed to have existed in
the early universe, just microseconds after the Big Bang. It is an exotic form ofmatter in which quarks and gluons, the fundamental building blocks of matter, aredeconfined and behave as free particles rather than being bound within hadrons suchas protons and neutrons. QGP is characterized by extremely high energy densities
and temperatures, surpassing those found in ordinary nuclear matter.

10

The existence of this novel phase of matter was proposed in the mid-seventies,
just ten years after the birth of the Quark Model of hadrons, and two years after
it was realized that the candidate non-Abelian field theory of inter-quark forcespredicted by QCD of their weakening at short distances, the so-called asymptotic
freedom [10].
It can be said that QGP emerges as the new phase of strongly interacting matter,manifesting its physical properties in terms of nearly free dynamics of practicallymassless gluons and quarks. Both quarks and gluons must be present in conditions
near chemical equilibrium with their color charge open for a new state of matter tobe referred to as QGP.
An early discovery was that the quark-gluon plasma behaves more like a perfect
fluid with small viscosity than like a gas, as many researchers had expected.
In the QGP state, the strong force, described becomes weakened, and quarks
and gluons interact weakly with each other. This transition from ordinary hadronicmatter to the QGP phase occurs at extreme temperatures and densities. By reachingthese conditions in laboratory experiments, such as those conducted at the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory and the Large
Hadron Collider (LHC) at CERN, scientists aim to study the properties and behavior
of QGP [11].

Figure 1.5: QGP state in schematic phase diagram of QCD [2]

11

1.3

D and B mesons in Heavy Ion Collisions

B mesons are mesons composed of a bottom antiquark and either an up quark (B + ),a down quark (B0 ), a strange quark (B0s ), or a charm quark (Bc+ ). The combinationof a bottom antiquark with a top quark is not thought to be possible due to the top
quark’s short lifetime. Similarly, a combination of a bottom antiquark with a bottom
quark forms bottomonium, which is a distinct entity. These mesons are importantin the study of particle physics, as they are considered as hard probes of the QGP.
Researchers prefer to focus on heavy flavors (HF) since open HF states tend to becontaminated by light quarks.
The B meson has a mass of approximately 5.3 GeV/c2 , which is roughly fivetimes the mass of a proton. It undergoes decay processes into various particles,
including photons, lighter mesons, or leptons.
Beauty quark was discovered in 1977 with the discovery of upsilon meson
Y(JP C = 1−− ) as a narrow resonance at Fermi Lab and the currently measured
mass is 9460.37 ± 0.21 MeV and width 52.5 ± 1.8 keV. The beauty quark is assigned quantum number B=±1. The B = -1 states (B̄ 0 , B − )B̄s0 form an SU(3)triplet (3̄) and B = -1 states (B + ,B0 )B0s form another triplet (3). The masses anddecay time of B − mesons (from PDG) are given below:
B± = 5279.16 ± 0.31 MeV, τ = (1.638 ± 0.11) x 10−12 s and
B± = 5279.53 ± 0.33 MeV, τ = (1.530 ± 0.069) x 10−12 s
The D meson on the other hand is a specific type of meson that consists of acharm quark and a lighter quark or antiquark. As a hadron, it is composed of quarks
and gluons, specifically a charm quark and a lighter quark or antiquark. With a massof around 1.86 GeV/c2 , the D meson is approximately twice as heavy as a proton.
It can decay into different particles such as photons, lighter mesons, or leptons.
D mesons are significant as they are the lightest particles containing charm
quarks. They are often studied to gain insights into weak interactions. Prior to1986, the strange D mesons were referred to as "F mesons."
Since D mesons are the lightest mesons with a single charm quark (or antiquark),
they must undergo charm (anti)quark flavor-changing transitions to decay. Thesetransitions involve a change in the internal charm quantum number and occur solely
through the weak interaction. In D mesons, the charm quark primarily transforms
into a strange quark via the exchange of a W boson. Consequently, D mesons
predominantly decay into kaons (K) and pions (π).
The charm quark is assigned a quantum number C = 1 and C = 0 for up, down,
and strange quarks. The charge of charm quark is 2/3 and its mass mc = (1/2)mJ/ψ
= 1.55 GeV. All charmed mesons are listed in Table . . . D+ , D0 , Ds+ (D0 , D− , Ds− )decay weakly while J P = 1− D-meson decay strongly and radiatively.

12

Figure 1.6: Properties of D meson types [3]1.3.1Importance of study of B and D mesons

The study of charm and beauty quark decays provides valuable insights into fundamental parameters of the Standard Model, such as the elements of the CabibboKobayashi-Maskawa (CKM) matrix. Furthermore, it contributes to our understanding of CP (charge-parity) violation.
There are several importances of the study of separation of charm and beauty
quarks in the field of heavy ion collisions:
1. The separation between charm and beauty quarks plays a crucial role in investigating the energy loss and transport properties of heavy quarks in the
QGP. When heavy quarks traverse the QGP, they experience interactions withthe surrounding medium, resulting in energy loss. The energy loss mechanisms for charm and beauty quarks differ due to their distinct masses, leading
to different patterns of energy degradation. By measuring the energy loss ofcharm and beauty quarks, scientists can extract valuable information about
the QGP transport properties and gain insights into its viscosity and otherrelevant parameters [12].
2. Quarkonium states, such as J/ψ and Υ, which consist of a heavy quarkantiquark pair (c¯c and b¯b, respectively), are sensitive to the propertiesof the QGP. The separation between charm and beauty quarks is crucial forunderstanding the suppression of quarkonium states in the QGP. The binding
13and dissociation of quarkonium states are affected by the color screening andinteractions with the QGP medium. The suppression patterns of charmonium
and bottomonium states provide valuable information about the temperatureand properties of the QGP, enabling us to explore the phase transition from a
hadronic medium to the QGP [13].
3. Reconstructions of muon tracks is important to separate the beauty contribution from that of charm quarks. At the LHC, single muons are mainly produced through D hadrons, B hadrons and light hadrons. q Studies of charmed
and beauty sectors in Pb-Pb collision will provide information on the fragmentation, hadronization of heavy quarks and new information on weak annihilation process [14].
4. The charm/beauty separation will enable us to investigate experimentally themass, colour charge dependences, and energy loss of heavy quarks, lightquarks and gluons Two other benefits of separation: (1) Providing importantreference for the study of charmonium (or bottomonium) suppression and regeneration in QGP. (2) Comprehensive understanding of in-medium partonenergy loss and thermalization [15].
5. Heavy quarks, due to their large masses, exhibit a reduced diffusion coefficient compared to light quarks in the QGP. The separation between charmand beauty quarks allows for the study of the mass dependence of heavy quark
diffusion, providing insights into the interplay between the QGP medium andheavy quark dynamics [16].
6. Charm and beauty quarks have different interaction rates with the QGP due
to their mass difference. Studying their thermalization processes contributes
to our understanding of the dynamics of heavy quarks in the QGP medium
and the time scales associated with equilibration.

1.3.2

Decay Process of D and B mesons

The decay of a D meson into a muon typically proceeds through the weak interaction mediated by the exchange of a W boson. There are several possible decay
channels for a D meson into a muon, depending on the specific type of D meson
involved.
One of the most common decay channels for a neutral D meson (D0 ) into amuon is the so-called semileptonic decay, where the D meson decays into a muon
and its associated neutrino. The specific decay process is: D0 →µ− + vµ . In thisdecay, the D0 meson, which consists of a charm quark (c) and an up antiquark (u),14transforms into a muon (µ− ) and its associated neutrino (vµ ). It’s important to notethat the D0 meson can also undergo flavor-changing decays, where it transforms intoa strange meson (K meson) and a muon-antimuon pair. However, the semileptonic
decay channel described above is the dominant one.
For charged D mesons (D+ and Ds+ ), the main decay channel into a muon involves the decay of the charm quark (c) into a strange quark (s) and the emission ofa W boson. The specific decay process for a charged D meson is:
D+ → µ+ +vµ
Ds+ → µ+ + vµ
In these decays, the charged D meson transforms into a muon (µ+ ) and its associated neutrino (vµ ) by changing the charm quark (c) into a strange quark (s) throughthe weak interaction.
It’s important to note that there can be additional secondary decay processes
occurring after the initial D meson decay, such as the subsequent decay of the produced muon. However, the main decay channel of a D meson into a muon involves
the direct transformation of the charm quark into a muon, accompanied by the emission of a W boson.
The branching ratio of a specific decay channel represents the probability thata particle will decay through that channel compared to all possible decay channels.
The branching ratio is typically given as a percentage or a fraction.
For the decay D0 →µ− + vµ , the branching ratio is estimated to be around 6.9%.
This means that approximately 6.9% of all D0 mesons that decay will do so throughthis specific channel.
For the decays D+ → µ+ +vµ and Ds+ → µ+ + vµ , the branching ratios areapproximately 17% and 6%, respectively. These values indicate that around 17%of all D+ mesons and 6% of all Ds+ mesons that decay will undergo these specificdecay channels.

1.3.3

Main Decay Channel of D and B Mesons

The main decay channel of a B meson into a muon typically involves the weak interaction mediated by the exchange of a W boson. Similar to the decay of D mesons,
there are several possible decay channels for a B meson into a muon, depending on
the specific type of B meson involved.
One of the most common decay channels for a neutral B meson (B 0 ) into a muonis the semileptonic decay, where the B meson decays into a muon and its associatedneutrino. The specific decay process is: B 0 → µ− + vµ . In this decay, the B 0meson, which consists of a bottom quark (b) and an up antiquark (u), transforms
into a muon (µ− ) and its associated neutrino (vµ ). This semileptonic decay channel

15

is the dominant one for neutral B mesons.
For charged B mesons (B + and Bs0 ), the main decay channel into a muon involves the decay of the bottom quark (b) into a charm quark (c) and the emission ofa W boson. The specific decay processes for charged B mesons are:B + → µ+ + vµ , and Bs0 → µ+ + vµ .
In these decays, the charged B meson transforms into a muon (µ+ ) and its associated neutrino (vµ ) by changing the bottom quark (b) into a charm quark (c)through the weak interaction.
It’s important to note that there can be additional secondary decay processes
occurring after the initial B meson decay, such as the subsequent decay of the produced muon. However, the main decay channel of a B meson into a muon involves
the direct transformation of the bottom quark into a muon, accompanied by theemission of a W boson.
For the decay B 0 → µ− + vµ , the branching ratio is estimated to be around 1.1%.
This means that approximately 1.1% of all B 0 mesons that decay will do so throughthis specific channel.
For the decay B + → µ+ + vµ , the branching ratio is estimated to be approximately 10%. This indicates that around 10% of all B + mesons that decay will
undergo this specific decay channel.
For the decay Bs0 → µ+ + vµ , the branching ratio is estimated to be around
1.1% as well. This means that approximately 1.1% of all Bs0 mesons that decay will
undergo this specific decay channel.

Figure 1.7: Another decay channels of B and D mesons into another mesons.
Branching ratio of both decays: B(B + → D0 l+ vl ) = 2.35 ± 0.09% and B(D+ →K + µ+ vµ ) = 8.76 ± 0.19% [4].

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1.4

Purpose of the study

The main objective of this study is to identify decay muons originating from B
mesons and D mesons in heavy-ion collisions. We measure the decay of B andD mesons into muons to extract various decay parameters, including meson decay
vertices (x, y), decay angles (θ, ϕ), transverse momentum (pT ), and the distance of
closest approach (DCA). To reconstruct the particles’ tracks, we utilize collision
simulations from PYTHIA 8 and the O2 (Online-Offline) framework.
This paper focuses primarily on the measurement of pT and DCA, as they provide valuable information about the displacement of muons from the collision vertex (also known as the primary vertex). The distinct lifetimes of beauty and charm
decays result in differences in the displacement. We anticipate that the parametersof pT and DCA will offer clear distinctions between various types of decay muons,
allowing us to distinguish between signal muons (originating from B and D mesons)and background muon sources (such as kaons, pions, and others).

17

Chapter 2
Simulation and Experimental
Circumstances
2.1

ALICE Experiment

Figure 2.1: Layout of Large Hadron Collider (LHC)The ALICE (A Large Ion Collider Experiment) is one of the major experiments
conducted at the Large Hadron Collider (LHC), located at CERN (European Organization for Nuclear Research) in Geneva, Switzerland. ALICE focuses on studying
the properties of matter created in ultra-high-energy heavy-ion collisions.
ALICE was specifically designed to investigate the behavior of QGP, exploring
the phase transition toward the normal hadronic matter. In Run 3 the focus is on
18

Figure 2.2: Components of ALICE Run-3 [5]rare processes such as production of heavy-flavour particles, quarkonium states, realand virtual photons and heavy nuclear states. This grand experiment will hopefully
lead to the study of the production of exotic particles and search for new physics
phenomena. The ALICE detector is a complex and sophisticated apparatus designed
to measure the various particles produced in heavy-ion collisions. It consists ofseveral sub-detectors, each serving a specific purpose.
The Run 3 of the LHC, which includes the ALICE experiment, is scheduled
to take place in the 2021-2024 timeframe. In Run 3, the collision energy in the√
beam center-of-mass system can reach sN N = 13.6 TeV for proton-proton (pp)√collisions and sN N = 5.5 TeV per nucleon pair for Pb-Pb collisions, which are thehighest collision energies in the world. ALICE detector is about 10 000-tonne– 26
m long, 16 m high, and 16 m wide. The detector sits in a vast cavern 56 m below
ground close to the village of St Genis-Pouilly in France, receiving beams from the
LHC.
The Run 3 of the LHC is capable of enhancing the particle measurement in lowtransverse momentum, pT region and improve particle tracking capability, allowing data taking at substantially higher rates and preserving the already remarkableparticle identification capabilities from Run 1/Run2.

2.2

Muon Forward Tracker (MFT)The MFT (Muon Forward Tracker) is a high-resolution silicon-tracking detector
designed to reconstruct the trajectories of muons emitted at forward angles during heavy-ion collisions. It is a silicon pixel detector positioned upstream of the19hadron absorber, with a coverage spanning -3.6 < η < -2.5 and encompassing a fullazimuthal angle range.
The primary motivation behind integrating the MFT into the ALICE setup is
to align the extrapolated muon tracks originating from the tracking chambers postabsorber with the clusters detected in the MFT planes pre-absorber. This alignment
facilitates accurate associations between the muon tracks and MFT clusters, resulting in improved pointing accuracy of the muon tracks. Consequently, a reliablemeasurement of their offset relative to the primary vertex of the interaction becomesachievable.
The MFT consists of two half cones, each equipped with five half-disks positioned along the beam axis between -46 cm and -76.8 cm from the interaction point.
This configuration allows for precise measurements of charged particle passages
and provides valuable information regarding the distance of closest approach of themuon track to the primary vertex.
Furthermore, the MFT’s capabilities are essential for c/b muon separation, enabling the distinction between charm (c) and bottom (b) muons based on theirunique trajectories and interactions within the detector [17]. Such functionality contributes significantly to the accurate identification and analysis of muons emitted inheavy-ion collisions within the ALICE experiment.

Figure 2.3: MFT half disks

20

Figure 2.4: MFT Geometry

2.3

Muon Spectrometer

The Muon Spectrometer, aside from MFT is also primarily responsible for measuring the trajectories and momenta of muons produced in heavy-ion collisions. The
invariant mass resolution of the muon spectrometer is of the order of 70 MeV in
the J/Ψ region and about 100 MeV close to the Υ. These values allow to resolve
and measure individually all five resonance states. The main components of thespectrometer are shown in Figure xx below.
Components of the muon spectrometer consists of an absorber to filter the background, a set of tracking chambers before, inside and after the magnet and a set of
trigger chambers.
1. The front absorber suppresses all particles except muons coming from theinteraction vertex. It is made of carbon and concrete in order to reduce themultiple scattering and muon energy loss. The inner beam shield protects thechambers from background originating from particles at small angles. It is
made of tungsten, lead and stainless steel to reduce the background arisingfrom primary particles emitted in the collision and from their showers produced in the beam pipe and in the shield itself.
2. The tracking system is made of 10 cathode pad/strip chambers arranged in
5 stations of 2 chambers each. The spatial resolution should be better than
100 mm and all the chambers are made of composite material to reduce thescattering of the muons in order to obtain the required resolution. To limit the21

occupation rate to a maximum of 5% the full set of chambers has more than
1 million channels.
3. The trigger system is designed to extract heavy quark resonance decays. The
selection is made on the pT of the two individual muons. The 4 planes of
RPCs (Resistive Plate Chambers) arranged in 2 stations and put behind apassive muon filter to measure the transverse momentum of each muon.
4. The dipole magnet is positioned at about 7 m from the interaction vertex and
it is one of the biggest warm dipoles in the world (free gap between poles ≈3 m, height of the yoke ≈ 9 m). The magnetic field (Bnom = 0.7 T, 3 Tm field
integral) is defined by the requirements on the mass resolution.

Figure 2.5: Layout of the muon spectrometer. The tracking system consists ofstations 1 to 5 and trigger system of stations 6 and 7 [6]2.4

O2 (Online-Offline) Data Framework

The O2 (Online Offline) data processing framework is a critical component of the
ALICE (A Large Ion Collider Experiment) project at CERN (European Organization for Nuclear Research). It serves a dual purpose, providing a unified framework
for both online data processing during data acquisition and offline data analysis to
extract meaningful physics results.
22Built on top of the widely-used ROOT data analysis framework, O2 leverages
ROOT’s rich set of tools and libraries while extending them to meet ALICE’s specific requirements. Its modular and scalable design enables distributed and parallel
processing across multiple computing nodes, following a component-based architecture that accommodates evolving experiment needs.
A key feature of O2 is its ability to perform real-time online data processing.
As raw detector data is continuously streamed, O2 processes and reconstructs it onthe-fly, offering immediate feedback and monitoring for informed decision-making
during data acquisition.
Online data processing in O2 involves stages such as data decoding, calibration,
reconstruction, and event building. Raw data is decoded to extract information, thencalibrated to correct for electronic noise and detector response. Event reconstruction identifies and measures individual particles and their properties. Finally, thereconstructed events are organized into larger structures for further analysis.
O2 provides a high-level interface and APIs for efficient data analysis, offering
tools and libraries for event reconstruction, particle identification, track finding,and data visualization. It also incorporates advanced data management capabilities,
utilizing distributed file systems and data caching to ensure efficient storage andretrieval of large-scale datasets.

Figure 2.6: Data volume reduction in O2 framework [7].

23Performance is optimized through modern computing technologies like multithreading and GPU acceleration, allowing timely analysis and quick feedback during the experiment.
The ALICE Computing Model for Runs 3 and 4 aims to reduce data volume
as early as possible during data-flow. Zero-suppressed data from all collisions will
be shipped to the O2 facility. Data volume reduction will be achieved throughsynchronous reconstruction steps during data taking, storing data temporarily athigh speeds.
The O2 facility will be a high-throughput system, utilizing heterogeneous computing platforms with hardware acceleration. The O2 software framework will provide abstraction for common code to function on different platforms, supporting
concurrent computing models across various facilities. Open-source software conforming to open standards will be used whenever possible, enhancing flexibility andadaptability.

2.5

Geant4

Geant4 is a toolkit used for the simulation of the passage of particles through matter,
including the interactions of particles with matter. It is used extensively in highenergy physics experiments, including those at the LHC, and is also used in otherfields such as medical physics and nuclear engineering.
In the context of O2, Geant4 is used to simulate the interactions of particles withthe detector material in both pp and Pb-Pb collisions. The simulation of pp collisions using Geant4 involves simulating the passage of particles through the various
layers of the detector, including the inner tracking detectors, the calorimeters, andthe muon chambers. The simulation takes into account the effects of particle interactions with the detector material, including energy loss due to ionization andradiation, as well as multiple scattering and particle decays.
To simulate complex process like p-Pb and Pb-Pb, Geant4 is also used in conjunction with other simulation tools such as the HIJING event generator and the
UrQMD hadronic transport model to simulate the evolution of the collision from its
initial stage to the final hadronic state. The simulation takes into account the effectsof particle interactions with the dense nuclear matter created in the collision, as wellas the propagation of particles through the detector material.
In both pp and Pb-Pb collisions, the output of the Geant4 simulation is a setof simulated detector hits, which can be processed in the same way as the realdetector data. This allows for a direct comparison of the simulated and measured
observables, and allows for the estimation of the detector acceptance, efficiency,
and resolution.
24

Furthermore, Geant4 also supports multiple visualization frameworks, including Open Inventor, OpenGL, and HepRep [18]. These frameworks allow for thecreation of 3D visual representations of the detector geometry and the simulatedparticle tracks.

2.6

Pythia 8

Pythia 8 is an important Monte Carlo event generator utilized extensively in ALICE experiment for both the simulation of collision events and the generation of
background events for data analysis. The program contains theory and models for anumber of physics aspects, including total and partial cross-sections, hard and softinteractions, parton distributions, parton showers for initial and final states, merging and matching of matrix elements and showers, interactions involving multiple
partons, hadronization/fragmentation, and decays. It is largely based on original
research but also incorporates numerous formulae and other knowledge from theliterature. The code package includes interfaces for input from and output to otherprograms, but the core can function independently, and it encompasses sufficient
tools to facilitate straightforward analyses. It also has a set of utilities and interfaces to external programs. Unlike previous version of the program, Pythia 8 iswritten in programming language of C++.
Strengths of Pythia 8 lie in specialized physics models, detailed parton showers,
and hadronization simulations. However, it does not simulate the detector response,and its event-by-event fluctuations might introduce limitations in precision studies.
So, Pythia 8 is used in conjunction with other simulation and reconstruction software (such as JETSET) to simulate the entire data acquisition and analysis process.
The simulated events generated by Pythia 8 serve as input to the ALICE detector
simulation, where they are propagated through the detector elements to simulate theparticle interactions and response of the detector. This step accounts for the detector resolution, efficiencies, and other instrumental effects, allowing for a realisticreconstruction of the measured data.
Over the last 40+ years, PYTHIA/JETSET has seen extensive use at almost all
high-energy colliders. These colliders include PETRA, PEP, TRISTAN, SLC, and
LEP for e+ e− collisions, HERA for ep collisions, Tevatron for pp collisions, and
LHC for pp/pA/AA collisions. PYTHIA/JETSET has also played a significant rolein the study of proposed future colliders, fixed-target experiments, and cosmic-ray
investigations, among others. Its versatility extends across all project stages, frominitial feasibility studies to detector design, search/analysis strategy development,
and data interpretation [19].

25

2.7

Track Matching

In the context of B and D meson decays, the primary goal is to match muon tracks
from these decays with their corresponding tracks in the ITS, TPC, and Muon Spectrometer. This crucial process allows for the accurate determination of the muon’s
momentum, decay vertex position, and other essential parameters.
Track matching in the ALICE detector involves several essential methods toidentify and associate tracks from the same particle. Pattern recognition algorithms
analyze detector responses, leveraging spatial information from ITS, TPC, and theMuon Spectrometer to identify common patterns and extrapolate tracks. Statistical techniques consider measurement uncertainties to determine track associations’likelihood, effectively minimizing misidentifications and false matches.
Ensuring reliable matched tracks, quality criteria evaluate parameters like track
fit chi-square, hit count, and overall compatibility with measured data. Particle
identification techniques play a crucial role in identifying muons, which is vital forstudying B and D meson decays. The Muon Spectrometer’s specific measurements
enable the distinction of muons from other particles based on energy deposition andtiming information.
Addressing the challenge of background and overlapping tracks is critical to
avoid interference with desired track identification. Algorithms are employed tomitigate these effects, incorporating energy loss measurements and track quality
criteria tailored for muons.
Multiple scattering and energy loss in detector materials present further challenges to track matching accuracy. To overcome these effects, corrections and calibration techniques are applied, ultimately enhancing the precision of track reconstruction.
In addition to traditional methods, advanced machine learning techniques areharnessed in the ALICE detector for track matching. Algorithms are trained onlarge datasets to learn correlations between detector responses and track parameters,
leading to improved efficiency and reduced misidentification rates.
The combination of these methods ensures accurate track associations, enabling
precise reconstruction of particle trajectories and supporting crucial studies in heavyion collisions within the ALICE experiment.
In LHC Run 3, the O2 framework takes center stage for simulations and analysis, with a specific focus on the decay of heavy flavor hadrons like B and D mesons
into muons (B, D→µX). Performance studies rely on HIJING for simulating background events, mainly composed of π, K particles, with signals from a PYTHIA
parametrization embedded within HIJING events. To provide an accurate representation of the experimental setup, the GEANT 4 detector simulation software is26

Figure 2.7: Track reconstruction in the Muon spectrometer and MFT

employed to simulate the interaction of generated particles with the ALICE detector. These tools play crucial roles in advancing our understanding of heavy flavor
decays and other phenomena in heavy-ion collisions within the ALICE experiment.

2.8

Global Forward Track

The global forward track reconstruction focuses on reconstruction of the tracks ofparticles emitted in the forward direction, specifically in the pseudorapidity, η rangeof the Muon Spectrometer (MCH and MTR) in ALICE. This region is of particularinterest for studying B and D meson decays because it allows for the identificationand measurement of muons, which are key signatures of these decays.
The first step in the global forward track reconstruction is pattern recognition.
The signals produced by the detector subsystems are analyzed to identify track segments corresponding to charged particles. In the case of muons, the MFT and Muon
Spectrometer is the primary subsystem used for their detection. The muons traverse
the various layers of the Muon Spectrometer, leaving energy deposits and triggering
the detector readout. The pattern recognition algorithms in the Muon Spectrometer identify these energy deposits and reconstruct track segments by connecting thehits in different detector layers. The reconstructed track segments are then used asa starting point for the subsequent track fitting process.
The next step is track fitting, where the reconstructed track segments are combined and fitted to determine the full trajectory of the muon track. The track fitting
algorithms use the information from the MFT and Muon Spectrometer layers to estimate the muon’s momentum, direction, and position at the primary vertex. The
fitted tracks are then used for further analysis and particle identification. In the case27

of muons, particle identification is accomplished by exploiting the specific characteristics of muons compared to other particles. The Muon Spectrometer subsystems,
MCH and MTR, provide measurements optimized for muon detection. By applying selection criteria based on energy deposition patterns, timing, and other relevant
parameters, the identification algorithms can effectively identify muon tracks anddistinguish them from other particles [20].
Quality selection criteria then are applied including parameters such as the number of hits in the MFT and Muon Spectrometer layers, the χ2 of the track fit, andthe track’s compatibility with the measured data. These criteria help filtering out
poorly reconstructed or misidentified tracks, improving the purity and quality of the
final track sample.

2.9

Monte Carlo (MC) track

MC tracks used in O2 for pp collisions are typically generated using the Pythia
event generator, which simulates the hard scattering of partons (quarks and gluons)in the colliding protons, as well as the subsequent hadronization and fragmentation
processes. The resulting particles are then propagated through the detector geometry using a Geant4-based simulation, which takes into account the effects of particle
interactions with the detector material, as well as detector response and resolution.
For Pb-Pb collisions, the MC tracks are generated using a hybrid approach thatcombines the HIJING event generator with the UrQMD hadronic transport model[21]. The HIJING generator simulates the initial hard scattering and parton-parton
interactions in the colliding nuclei, while the UrQMD model simulates the subsequent evolution of the system, including hadronization, resonance decays, and finalstate interactions. The resulting particles are then propagated through the detector
geometry using the same Geant4-based simulation as in the pp case.
In both cases, the MC tracks are used to provide a simulated baseline for comparison with the real data. The MC tracks are subjected to the same reconstruction
and analysis procedures as the real data, allowing for a direct comparison of themeasured and simulated observables. The MC tracks are also used to estimate thedetector acceptance, efficiency, and resolution, which are needed to correct the measured data for detector effects.
In addition to the standard MC tracks, O2 also employs a technique known asembedding, which allows for the simulation of specific physics processes within thecontext of real data events. In embedding, simulated particles are overlaid onto real
data events, allowing for the study of specific physics processes such as jet quenching or heavy-ion jet tomography. The embedded particles are generated using thesame Pythia or hybrid HIJING-UrQMD generators as in the standard MC tracks,28and are embedded into the real data events at the level of raw detector signals. The
resulting embedded events are then subjected to the same reconstruction and analysis procedures as the real data, allowing for a direct comparison of the embedded
physics process with the real data.

2.10

Transverse momentum (pT )The transverse momentum of a particle is the component of its momentum perpendicular to the beam axis, and it is a measure of the particle’s motion in the plane
perpendicular to the beam. In the study of heavy mesons such as B mesons and D
mesons, the measurement of the transverse momentum distribution provides valuable information on the production mechanism and properties of these particles.
The transverse momentum distribution of B mesons and D mesons reflects thedetails of the production mechanism. In particular, the transverse momentum distribution can provide information on the momentum transfer between the colliding
particles, which is related to the parton distribution functions (PDFs) of the colliding
particles. The PDFs describe the distribution of momentum and energy carried by
the partons (quarks and gluons) inside the colliding particles, and they are important
inputs for theoretical calculations of particle production in high-energy collisions.
In addition to providing information on the production mechanism, the transverse momentum distribution of B mesons and D mesons is also important for themeasurement of their properties. The transverse momentum affects the decay of theheavy mesons and the distribution of the decay products. For example, the transverse momentum of the B meson affects the opening angle between the two decay
products in the decay B → J/Ψ + K, which is used to measure the B meson lifetime.
The transverse momentum of the D meson affects the distribution of the decay products in the decay D → Kππ, which is used to measure the D meson decay constants
and form factors.
The transverse momentum of B mesons and D mesons can be measured using
various techniques, depending on the experimental setup. One common method is
to measure the curvature of the particle trajectory in a magnetic field. The transverse momentum is related to the curvature by the equation pT = qBρ, where pT
is the transverse momentum, q is the charge of the particle, B is the magnetic fieldstrength, and ρ is the radius of curvature. Another method is to measure the distribution of the decay products in the laboratory frame, and extract the transverse
momentum from the momentum conservation equation.

29

2.11

Distance of Closest Approach (DCAT )The Distance of Closest Approach (DCAT ) refers to the minimum distance between the trajectory of a charged particle and the primary vertex of the collision.
The quantity provides necessary information about the production, propagation, anddecay processes near the primary vertices. A muon track with a small DCAT indicates a close proximity to the primary vertex and a higher probability of originatingfrom a B or D meson decay. By measuring the displacement between the primaryvertex and the point of closest approach of the track, the decay length can also be
estimated. In this research, we measure the DCAT distribution of some particles
and decide certain ranges suitable for maximizing the muon signals extraction andrejecting most of background muons from other sources.

Figure 2.8: Distance of Closest Approach (DCA), the distance between a beam
center and a center of the circle [8].

2.11.1

DCA calculation with cartesian coordinate

If the exact position of the muon is not given, but we have information about the
decay point, muon momentum, muon momentum angles (horizontal and verticalangles), and primary vertex position, then we calculate the transverse distance of
closest approach (DCA) in the following way:
1. Convert the muon momentum and angles to momentum components: px =p ∗ sin(θ) ∗ cos(ϕ), py = p ∗ sin(θ) ∗ sin(ϕ), and pz = p ∗ cos(θ) where p

30

is the magnitude of the muon momentum, theta is the vertical angle, and ϕ isthe horizontal angle.
2. Calculate the direction vector of the muon in the transverse plane: vx = px /p,vy = py /p, and vz = 0.
3. Calculate the vector from the primary vertex to the decay point: dx = decayx −vertexx , dy = decayy − vertexy , dz = decayz − vertexz .
4. qCalculate the transverse momentum of the muon in the transverse plane: pT =
(p2x + p2y ). Calculate the transverse displacement vector from the primaryvertex to the track at a given radius r: r = pT /(0.3 ∗ B) where B is themagnetic field strength in Tesla.
5. Calculate the transverse distance of closest approach (DCA) in the transverse plane: DCAX = dx − r ∗ vx , DCAY = dy − r ∗ vy , and DCAT =p(DCA2X + DCA2Y ).
In these calculations, the muon position is not explicitly needed. Instead, muon
momentum, momentum angles, and primary vertex position are utilized to determine the DCA in the transverse plane.

31Chapter 3
Data Analysis – Extraction of Heavy
Flavour Decay Muons
The simulation data used in this research are the pp collisions at center of mass√√energy sN N = 14 TeV and Pb-Pb collisions at center of mass energy, sN N =5.52 TeV taken by ALICE Run 3 detectors. There is no minimum bias event andhigh multiplicity events (HM) were selected as collision types for both pp and PbPb collisions. There is no centrality specified for Pb-Pb collisions. We assume
that pp and Pb-Pb beams are directed at the primary vertex located at center of ITS
designated as (x, y, z) = (0,0,0) cm so that collisions occur at that point. But fromthe Figure 3.1, the primary vertices seem to form a sharp peak pattern below 0.05 cm
and are not fixed at (0,0,0) so the distance of closest approach calculation (DCA)
has to take this factor into account. The number of events in pp and p-Pb collisions
and parameters used in the simulations are summarized in the Table 3.1. Global
muon track and Monte Carlo tracks cover the MFT forward rapidity region (3.6 <η < 2.5), with muon momentum p > 4 GeV/c, and a magnetic field is applied in
the z-axis direction parallel to colliding particle beams. To identify the muons trackfrom B and D mesons decay, it is important to reconstruct the parameters (x, y, η, θ)of each detected muons so DCA of each decay point can be calculated.
Table 3.1: Simulation Set Up for pp and Pb-Pb collisions√
sN N [GeV] Number of Events
pp14
930,000
Pb-Pb
5.52
7,200

32

Figure 3.1: Primary vertices of particles in horizontal coordinate. There is a slightspreading of primary vertices beyond the the center of collision.

3.1

Expected Decay Muon Distribution in Global Muon
Track

The number of decay muons from Kaon (K + ), pions, D mesons, and B mesons
obtained from the Global Muon Track of pp collisions are shown in Table 3.2.
Table 3.2: Number of muon decay in GM Track of pp collisions
Decay [GeV] µ number
D→µ
1945
B→µ
227
+
π →µ
6842
K+ → µ3290
LK0 → µ72
τ− → µ43γ→µ5In this research, primary muons are defined to include muons decayed from nonhadrons (leptons, photons, W + , W − , W 0 , Z 0 , gluons, higgs, etc.) or any particles
with PDG codes less than 54. From the O2 simulation, we obtained 48 primarymuons produced from tau leptons and photons, with 43 and 5 each, respectively.
The peaks are mostly centered at DCAT = 0.15 cm. However, the DCAT distributions are not yielding clear different DCAT patterns for each mother particle
decaying into muons.
33T

dN/d(DCA )µ Track
DCAT All
GM
3

10

GMTrackDCAT_All
Entries
12685
Mean
0.189
Std Dev
0.178
All µ
K->µ
pi->µ
B->µ
D->µ

102

10

1

0

0.5

1

1.5

2
DCAT [cm]Figure 3.2: DCAT distribution of muons for GM Track of pp.

Table 3.3: Number of muon decay in GM Track of Pb-Pb collisions
Decay [GeV] µ number
D→µ
210
B→µ
13
π+ → µ1107
K+ → µ514
L0K0 → µτ− → µ20γ→µThe number of decay muons from kaon (K + ), pions, D mesons, and B mesons
obtained from the Global Muon Track of Pb-Pb collisions is shown in Table 3.3.
The statistics of muons obtained are fewer compared to pp, and it might not be
sufficient for DCAT histogram analysis. The formation of two peaks near DCAT =0.1 cm can be explained for a few reasons. When a meson decays into a muon, theresulting muon can have different lifetimes before it decays further. Some muons
from meson decays have relatively short lifetimes and decay close to the primaryvertex, resulting in a small DCAT value. On the other hand, some muons have
longer lifetimes and travel a significant distance from the primary vertex before
decaying, leading to a larger DCAT value.
We also calculate the DCA2T distribution to investigate whether there are specific pattern distributions of muon decay points in certain ranges. In certain cases,
the DCA2T distribution of B and D meson decays may exhibit distinct features orshow a specific pattern at certain DCA2T ranges. This can be due to the underlying
dynamics of the decays or the characteristics of the particles involved. From Figure34

T

dN/d(DCA )All µ
DCAT GM
Track
GMTrackDCAT_All
Entries
1894
Mean
0.2336
Std Dev
0.3363

102

All µ
K->µ
pi->µ
B->µ
D->µ

10

1
0

0.5

1

1.5

2
DCAT [cm]Figure 3.3: DCAT distribution of muons for GM Track of Pb-Pb

Figure 3.4: The formation of near uniform distribution in xy-plane at z=0 cm.

T

dN/d(DCA )22µ(DCAT)All
GMTrack

103

GMTrackDCATSquared_All

EntriesMean
Std Dev

12685
0.036220.04539All µK -> µπ -> µB -> µD -> µ

102

10

1

0

0.05

0.1

0.15

0.2

0.25

0.3
(DCA )2 [cm2]T

Figure 3.5: DCA2T distribution of muons for GM Track of pp

3.5, we can see an almost uniform distribution below 0.02 cm2 although that is not
the case for Figure 3.6. Firstly, this can be attributed to the finite lifetimes of B and35

T

dN/d(DCA )2µ2(DCAT)All
GMTrack
GMTrackDCATSquared_All

EntriesMean
Std Dev

102

1894
0.029240.05387

All µK -> µπ -> µB -> µD -> µ

10

1

0

0.05

0.1

0.15

0.2

0.25

0.3
(DCA )2 [cm2]T

Figure 3.6: DCA2T distribution of muons for GM Track of Pb-Pb

D mesons, as the muons originating from these decays would not have significant
displacements from the primary vertex. Second, multiple scattering of muons as
they traverse inside the material of the detector components can cause their trajectories to deviate, and they can no longer be reconstructed precisely to their decay
points. The resolution of the detectors in the detector system may not be as goodas the resolution in the tracker, leading to larger uncertainties in the DCA determination for global muon tracks. With no minimum bias setting applied in selection
criteria, there will be a pile-up of additional muon tracks contributing to overallsignals in the DCAT distribution.
Ideally, we expect a uniform distribution of decay muons near DCA2T = 0.
If the decay muons have a wide range of momenta, it increases the chances of
having small DCA2T values. This is because particles with different momenta will
have trajectories that are more likely to come close to each other, resulting in small
DCA2T values. Also, if decay muons are emitted isotropically in all directions, it
can lead to a more uniform distribution of DCA2T values. On the other hand, if
there are specific angular correlations between the particles, such as back-to-backemission, it can result in a more structured DCA2T distribution.

36

3.2

Expected Decay Muon Distribution in Monte Carlo
Track

The number of decay muons from kaons, pions, D mesons, and B mesons obtained
from Monte Carlo Track of pp collisions are shown in Table 3.4 and Table 3.5.
From Figure 3.7 and 3.8, it is shown that muons from B and D mesons dominate
at DCAT ≤ 0.1 cm with sharp peaks around DCAT = 0 cm, while muons fromkaons and pions dominate at DCAT ≥ 0.1 cm with lower distribution. The decay
processes of B and D mesons typically involve longer lifetimes, allowing the decaymuons to travel a certain distance before decaying. This can result in a broader
distribution of DCAT values, including a higher probability of observing muons
with larger DCAT values.

All µ
DCAT MCTrackTdN/d(DCA )Table 3.4: Number of muon decay in MC Track pp collisions
Decayµ number
D→µ
1919
B→µ
207
+
π →µ
2230
1050
K+ → µ
L
K0 → µ2741
τ− → µγ→µ6MCTrackDCAT_All
Entries5728
Mean
0.1884
Std Dev0.3693103All µK -> µπ -> µB -> µD -> µ

2

10

10

1

0

0.5

1

1.5

2

2.5
DCAT [cm]Figure 3.7: DCAT distribution of muons for MC Track pp

Figure 3.9 and 3.10 show the simulated distributions of decay muons for different pT ranges. The DCAT distributions of muons from D mesons and pions shows37

T

dN/d(DCA )Table 3.5: Number of muon decay in MC Track Pb-Pb collisions
Decayµ number
D→µ
1193
96
B→µ
π+ → µ1418
681
K+ → µ
K0L → µ
9
−
τ →µ
17
γ→µ
0
µ
DCATAllMCTrack103

MCTrackDCAT_All
Entries3582
Mean
0.1898
Std Dev0.3741All µK -> µπ -> µB -> µD -> µ

102

10

1
0

0.5

1

1.5

2

2.5

3
DCAT [cm]Figure 3.8: DCAT distribution of muons for MC Track Pb-Pb

an increasing trend toward a sharp peak near DCAT = 0 cm, while it remains considerably lower for B mesons and kaons. DCAT distributions generally decrease
with increasing pT due to particle kinematics and multiple scatterings in the detector
components. Higher pT particles have larger momentum and experience less curvature in the magnetic field, resulting in smaller DCAT . Multiple scattering causes
random deviations in the particle trajectories and can contribute to larger DCAT .
However, higher pT particles have larger momenta and are less affected by multiple
scattering, resulting in smaller DCAT . In case of B mesons, low count mainly thecause for lower distribution.
The DCAT mean and resolution are extracted by a Gaussian functional as
shown in Fig. 3.11 and Fig. 3.12. The DCAT mean for all muons (signals andbackgrounds) shows a decreasing trend, but it shows a somewhat decreasing trend
for beauty (decay muons from B mesons). Generally, higher pT particles tend to
be produced with larger transverse displacements from the primary collision vertex.
The presence of these secondary vertices can contribute to a larger average DCAT
for muons from charm compared to other particles. This might cause unclear trend38

µDCAT All
MCTrackTdN/d(DCA )T

dN/d(DCA )µDCATAllMCTrack
MCTrackDCAT_All
Entries1980
Mean
0.1509
Std Dev0.2039102

MCTrackDCAT_All
Entries2537
Mean
0.1219
Std Dev
0.1984102

All µK -> µπ -> µB -> µD -> µ10All µK -> µπ -> µB -> µD -> µ

10

1

1

0

0.2

0.4

0.6

0.8

1
DCAT [cm]

0

0.2

T

dN/d(DCA )MCTrackDCAT_All
Entries670
Mean
0.06329
Std Dev
0.1335

102

0.6

0.8

1
DCAT [cm]pT : 0.5-1.0 GeV/cµ
DCAT All
MCTrackTdN/d(DCA )pT : 0-0.5 GeV/c

0.4

All µ
K -> µπ -> µB -> µD -> µ10

All µDCAT MCTrack

102

MCTrackDCAT_All
Entries275
Mean
0.03745
Std Dev
0.08683
All µK -> µπ -> µB -> µD -> µ

10

1
1

0

0.2

0.4

0.6

0.8

1
DCAT [cm]

0

0.1

0.4

0.5

0.6
DCAT [cm]All µ
DCAT MCTrack

Tµ
DCATAllMCTrack
102

0.3

pT : 1.5-2.0 GeV/cdN/d(DCA )T

dN/d(DCA )pT : 1.0-1.5 GeV/c

0.2

MCTrackDCAT_All
Entries212
Mean
0.03111
Std Dev
0.06738
All µ
K -> µπ -> µB -> µD -> µ10

MCTrackDCAT_All
Entries48
Mean
0.018540.02456
Std Dev

10All µK -> µπ -> µB -> µD -> µ

1

1

0

0.1

0.2

0.3

0.4

0.5

0.6
DCAT [cm]TdN/d(DCA )pT : 2.0-4.0 GeV/c

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14
DCAT [cm]pT : 4.0-7.0 GeV/c

All µ
DCAT MCTrack103

MCTrackDCAT_All
Entries5728
Mean
0.1169
Std Dev0.1917All µK -> µπ -> µB -> µD -> µ

102

10

1

0

0.2

0.4

0.6

0.8

1
DCAT [cm]pT : 0-10.0 GeV/c
Figure 3.9: DCAT distributions of decay muons in different pT ranges for MC trackof pp.
for the charm. On the other hand, the DCAT resolution trends show no particulardependence on particle types and generally decrease with increasing pT as a bias ofa tracking algorithm.
39

Table 3.6: Decay muons count in different pT ranges for MC track of pp.
pT [GeV] Charm Beauty All µ
0-0.5
364
7
1980
872
55
2537
0.5-1.0
1.0-1.5
363
38
670
180
24
275
1.5-2.0121
53
212
2.0-4.0
4.0-7.0
15
29
48
1919
207
5728
0-10.0µDCATAllMCTrackTdN/d(DCA )T

dN/d(DCA )µDCATAllMCTrack
MCTrackDCAT_All
Entries1265
Mean
0.1565
Std Dev
0.212

102

MCTrackDCAT_All
Entries1577
Mean
0.1225
Std Dev0.2055All µK -> µπ -> µB -> µD -> µ

2

10

All µ
K -> µπ -> µB -> µD -> µ

10

10

1

1
0

0.2

0.4

0.6

0.8

1
DCAT [cm]00.2

pT : 0-0.5 GeV/c

0.6

0.8

1
DCAT [cm]pT : 0.5-1.0 GeV/c

T

TµDCATAll
MCTrackdN/d(DCA )All µ
DCAT MCTrackdN/d(DCA )0.4

MCTrackDCAT_All
Entries430
Mean
0.06541
Std Dev
0.1483102

MCTrackDCAT_All
Entries158
Mean
0.04104
Std Dev
0.0818All µK -> µπ -> µB -> µD -> µ10All µK -> µπ -> µB -> µD -> µ

10

1

1

0

0.2

0.4

0.6

0.8

1
DCAT [cm]00.1

pT : 1.0-1.5 GeV/c

0.2

0.4

0.5

0.6
DCAT [cm]pT : 1.5-2.0 GeV/c

T

Tµ
DCAT All
MCTrackdN/d(DCA )µDCATAllMCTrackdN/d(DCA )0.3

MCTrackDCAT_All
Entries138
Mean
0.03678
Std Dev
0.08866

MCTrackDCAT_All
Entries12
Mean
0.02042
Std Dev0.01346

All µK -> µπ -> µB -> µD -> µ10All µK -> µπ -> µB -> µD -> µ

1

1

0

0.1

0.2

0.3

0.4

pT : 2.0-4.0 GeV/c

0.5

0.6
DCAT [cm]

0

0.02

0.04

0.06

0.08

0.1
DCAT [cm]pT : 4.0-7.0 GeV/c40

TdN/d(DCA )µDCAT All
MCTrack
MCTrackDCAT_All
Entries3581
Mean
0.1181
Std Dev
0.1945All µK -> µπ -> µB -> µD -> µ

102

10

1

0

0.2

0.4

0.6

0.8

1
DCAT [cm]pT : 0-10.0 GeV/c
Figure 3.10: DCAT distributions of decay muons in different pT ranges for MC
track of Pb-Pb.
Table 3.7: Decay muons count in different pT ranges for MC track of Pb-Pb.
pT [GeV] Charm Beauty All µ
0-0.5
228
3
1265
546
25
1577
0.5-1
231
24
430
1-1.5
1.5-2
98
16
158
80
25
138
2-4
4-7
9
3
12
1193
96
3581
0-10

(a)

(b)

Figure 3.11: (a) Mean and (b) resolution for MC track of pp.

(a)

(b)

Figure 3.12: (a) Mean and (b) resolution for MC track of Pb-Pb.
41

3.3

Signals and Background Muons in Monte Carlo
Track

Fig. 3.13 and 3.14 show the simulated pT distribution of signal muons (from charmand beauty) and background muons (from other sources) for pp and Pb-Pb collisions. Signal muons dominate background muons for the region pT ≥ 1 GeV/cbecause heavy flavor mesons like B and D mesons have larger masses and, therefore, their decay muons tend to carry higher momenta.
At pT ≥ 1 GeV, background muons dominate signal muons, caused by decays oflighter mesons, hadronic interactions, and low-energy processes. Fig 3.15 and 3.16
show the simulated DCAT distribution of signal muons and background muons√
3(top diagram), with the bottom diagrams showing the BS cut-off distribution. The
S(signal-to-background) ratio is a useful metric for determining the cut-off condiBtion in the shift of the generating point. However, when the cut-off is applied, thenumber of signal events decreases, leading to a loss of statistical significance. To√
address this issue, the BS ratio is weighted by S, representing the statistical signif√
3
icance of the signal. This weighted ratio, known as BS , is used as an index. The√
3highest point of the BS distribution serves as a reference, considering the detection
efficiency of the signal. This reference point, along with the efficiency, is examined
to determine the optimal conditions for the final shift of the generating point. By
incorporating both the statistical significance of the signal and the efficiency of detection, a comprehensive evaluation of the shift is performed to ensure reliable andaccurate results.
From figures 3.15 and 3.16, it can be inferred that the count of signal muons rises
above the background muons, reaching a maximum significant value near DCAT =0.03 cm. This indicates that the signal events are more significant compared to thebackground events near that specific point.
The peak occurring near zero suggests that the separation between the signal andbackground distributions is particularly noticeable at low values or close to the origin of the variable being analyzed. This region could correspond to a specific physical phenomenon or process of interest where the signal stands out prominently,
making it easier to distinguish from the background. We use this information todecide on the suitable cut-off range to separate heavy flavor muons from the background: the DCAT < 0.03 cm region is the most suitable.

42

(a)

(b)
Figure 3.13: pT distributions for signal muons and background muons for MC trackof (a) pp and (b) Pb-Pb

43

Signalµ Track
pp DCAMC
T

dN/d(DCA )T

MCTrackDCAT_All_S
Entries2126
Mean
0.02548
Std Dev0.04173

102

Signal µ
Background µ

10

1

0

0.1

0.2

0.3

0.4

0.5
DCAT [cm]0.4

0.5
DCAT/GeV/c

(a)
3

S3/B

S /B for pp collisions
120

100

80

60

40

20

0

0

0.1

0.2

0.3

(b)
Figure 3.14: (a) DCAT distributions for signal muonsand background muons for√
S3
MC track of pp and their (b) statistical significance, B distribution.

44

T

dN/d(DCA )SignalMCµ Track
Pb-Pb DCAT
MCTrackDCAT_All_S
Entries1289
Mean
0.02266
Std Dev
0.033
102

Signal µ
Background µ

10

1
0

0.1

0.2

0.3

0.4

0.5
DCAT [cm]

(a)
3

S3/B

S /B for pp collisions
100

80

60

40

20

0

0

0.1

0.2

0.3

0.4

0.5
DCAT/GeV/c(b)
Figure 3.15: (a) DCAT distributions for signal muons andbackground muons for√
S3
MC track of Pb-Pb and their (b) statistical significance, B distribution45

Signal µ

pp p MC Track below DCAT < 0.03 cm
T

dN/d(p )T

MCTrackPt_S_below_003

160

EntriesMean
Std Dev

140

1616
1.062
0.7081Signal µ
Background µ

120
100
80
60
40
20
0
0

0.5

1

1.5

2

2.5

3

3.5

4

4.5
p [cm]
T(a) pT distributions for signal muons and background muons for MC track of pp
after cut-off optimization (DCAT < 0.03 cm). Background muons are significantly
reduced and proportion of signals are maximized.
All µpp p MC Track with DCAT < 0.03 cm

dN/dp

T

T

MCTrackPt_All_below_003

EntriesMean
Std Dev

1021156
0.85230.572

All µK -> µπ -> µB -> µD -> µ

10

1

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5
p [GeV/c]
T(a) pT distributions for all muons for MC track of pp after cut-off optimization(DCAT < 0.03 cm). Decay muons from K and π are significantly reduced, while
proportion from B and D mesons are maximized.
Figure 3.16: pT distributions result for pp after applying cutoff only for region
DCAT < 0.03 cm

46

Signal µ

Pb-Pb p MC Track below DCAT < 0.03 cm
T

dN/d(p )T

MCTrackPt_S_below_003

120

EntriesMean
Std Dev

100

1000
1.044
0.7057Signal µ
Background µ

80

60

40

20

0
0

0.5

1

1.5

2

2.5

3

3.5

4

4.5
p [cm]Ta) pT distributions for signal muons and background muons for MC track of Pb-Pb
after cut-off optimization (DCAT < 0.03 cm). Background muons are significantly
reduced and proportion of signals are maximized.
All µPb-Pb p MC Track with DCAT < 0.03 cm

dN/dp

T

T

MCTrackPt_All_below_003

102

EntriesMean
Std Dev

747
0.87
0.5524All µK -> µπ -> µB -> µD -> µ

10

1

0

0.5

1

1.5

2

2.5

3

3.5

4
p [GeV/c]
T(a) pT distributions for all muons for MC track of Pb-Pb after cut-off optimization(DCAT < 0.03 cm). Decay muons from K and π are significantly reduced, while
proportion from B and D mesons are maximized.
Figure 3.17: pT distributions result for Pb-Pb after applying cutoff only for region
DCAT < 0.03 cm

47

3.4

Prompt decay muons from light mesons

Figure 3.18 and 3.19 show pT and DCAT distributions of light mesons (ρ, ω, η, andϕ mesons). Prompt decay muons are muons that arise directly from the primary collision vertex in high-energy particle collisions. They are distinct from non-prompt
muons, which originate from the decay of long-lived particles or secondary decay
vertices.
The production of prompt decay muons from the primary vertex is intricately
linked to the properties and interactions of the light mesons such as ρ, ω, η, andϕ mesons. Their lifetimes are relatively short compared to heavy flavor mesons
and are typically on the order of 10−23 to 10−24 s. This short lifetime is due to the
decay processes associated with the weak interaction and the relatively small massdifferences between the mesons’ constituent quarks. These mesons are typicallyshort-lived and decay rapidly, giving rise to prompt muons. The decay of heavy
flavor mesons occurs via weak interactions, which are mediated by the exchange of
W and Z bosons.
Another characteristic of prompt decay muons is their straighter trajectory compared to other charged particles. This is due to the fact that muons are not strongly
affected by electromagnetic interactions within the detector material, unlike charged
hadrons such as pions or kaons. Muons, being leptons, only interact weakly withthe detector material and experience minimal energy loss through ionization or radiation. As a result, prompt muons tend to exhibit minimal scattering and follow
relatively straight paths from the primary vertex to the muon detectors.

48

Table 3.8: Parent particles and their decay muon count in pp.
Meson Types µ Count
η
76
63
ω
ρ
32
6
ϕ
Total
177
Table 3.9: Parent particles and their decay muon count in Pb-Pb.
Meson Types µ Count
η
42
60
ω
ρ
17
ϕ
8
Total
127

49

(a)

T

dN/d(DCA )-> µ
DCATρMCTrack
102

MCTrackDCAT_rho
Entries
32
Mean
0.001
Std Dev0.005568

ρ -> µω -> µη -> µφ -> µ

10

1

−0.01

−0.005

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035
DCAT [cm](b)
Figure 3.18: (a) pT and (b) DCAT distributions of prompt decay muons from lightmesons for MC track of pp.

50

(a)
ρ -> µTdN/d(DCA )DCAT MCTrack
MCTrackDCAT_rho
Entries17−6.679e−16
Mean0
Std Dev

ρ -> µω -> µη -> µφ -> µ10−0.01 −0.008 −0.006 −0.004 −0.002

0

0.002

0.004

0.006

0.008

0.01

0.012 0.014
DCAT [cm](b)
Figure 3.19: (a) pT and (b) DCAT distributions of prompt decay muons from lightmesons for MC track of Pb-Pb.

51

Chapter 4
SummaryThis paper focuses on the study of charm and beauty mesons’ decay muons in the
ALICE detector at CERN. The analysis strategy involves using the offset and distance of closest approach (DCA) quantities, along with other kinematic parameters,
to estimate the decay length of B and D mesons, which is related to their lifetime.
The study primarily relies on simulations to investigate various aspects. The primary analysis technique for measuring charm and beauty yields in the single muonchannel is based on fitting the DCAT distribution. Proper DCAT results were obtained from a simulated kinematic MC file, proving its reliability compared to MFT
and GM files for DCAT calculations. The MC track simulation has shown better
DCAT distribution results for B and D mesons compared to GM track simulation,suggesting the influence of multiple scattering in ALICE detector components affecting accuracy of the track matching.
At DCAT > 0.1 cm, muons from pion and kaon decays dominate the DCAT distribution with mostly wider distribution, while at pT > 1 GeV/c, muons from charmhadron and beauty-hadrons decays dominate for small DCAT values (DCAT < 0.1
cm). Cutoff optimization with pT and DCAT information were conducted, and signal yield and background calculations are obtained. The analysis has shown a highstatistical significance of signal muons compared to background muons near DCAT
< 0.03 cm so we have decided that would be a good separation point. To separate
muons from pions and kaons below DCAT < 0.03 cm and maximize signals (fromB and D mesons) while filtering background (from other muons), rejection techniques are implemented. As a result, B mesons and D mesons decay muons havebeen well separated from pions and kaons in the low transverse momentum region(pT ≤ 4 GeV/c). Additionally, prompt muons from light mesons (ρ, ϕ, η, and ω
mesons) are concentrated at DCAT = 0 cm.
However, the low count of B mesons prevents meaningful analysis, while thehigh count of charm mesons requires rejection of minimum bias from charmonium
for better accuracy. The true matching also needs to be calculated to see how false52matching tracks can impact the efficiency of the overall measurement.
In the actual experiment, Muon Forward Tracker (MFT) plays a crucial role inthe study of charm and beauty mesons’ decay muons by providing vertexing capabilities at forward rapidity. It effectively separates muons from charm-hadron andbeauty-hadron decays from the light-flavor background, with respective pT cutoffs
of about 0.5 GeV/c and 2 GeV/c. Notably, the longer lifetime of beauty hadrons results in muons from beauty-hadron decays having a larger average separation fromthe primary vertex compared to those from charmed hadrons.
Promising performance studies have demonstrated successful separation of muons
from charm and beauty decays at forward rapidity using real data from LHC Run 3.
The ongoing analysis involves pp collisions at 13.6 TeV collected in 2022, and datataking for Pb-Pb collisions at 5.36 TeV is expected in October 2023.

53

Bibliography[1] Povh, R., Rith, K., Scholz, C., and Zetsche, F. Particles and Nuclei: An
Introduction to the Physical Concepts. Springer, 2015.
[2] The CMS Collaboration. CMS Physics Technical Design Report: Addendum
on High Density QCD with Heavy Ions, volume 34. Journal of Physics G, Oct2007. URL https://iopscience.iop.org/article/10.1088/
0954-3899/34/11/008.
[3] Fayyazuddin Riazuddin. A Modern Introduction to Particle Physics. World
Scientific, 2011.
[4] P. A. Zyla and et al. (Particle Data Group), editor. Review of Particle Physics.
Particle Data Group, 2020. Available online at http://pdg.lbl.gov/
2020/reviews/rpp2020-rev-passage-particles-matter.
pdf.
[5] Tauro, Arturo. ALICE Schematics. Number ALICE-PHO-SKE-2017-001 in
ALICE Photos / ALICE / ALICE Sketches. CERN, 2017. URL https://cds.cern.ch/record/2263642. General Photo.
[6] Christian Finck for ALICE Muon Spectrometer Collaboration. The Muon
Spectrometer of the ALICE, volume 50. Institute of Physics Publishing, 2006.
doi: 10.1088/1742-6596/50/1/056. Proceedings of the 5th International Conference on Physics and Astrophysics of Quark Gluon Plasma.
[7] ALICE-O2 Collaboration. ALICE O² Project. 2018. URL https://alice-o2-project.web.cern.ch/alice-o2-project. Submitted by gpachoud on May 16, 2018, Accessed on July 24, 2023.
[8] Kazuya Nagashima. Energy Loss of Charm and Bottom Quarks in Quark√Gluon Plasma Created in Au+Au Collisions at sNN = 200 GeV. 2019.
[9] F. J., Ynduráin. Quantum Chromodynamics: An Introduction to the Theory of
Quarks and Gluons. Springer, 2006.
[10] Pasechnik, Roman and Šumbera, Michal. Phenomenological Review on
Quark–Gluon Plasma: Concepts vs. Observations. 2017. URL https://arxiv.org/abs/1611.01533v3. arXiv:1611.01533v3 [hep-ph].
[11] CERN.
Heavy Ions and Quark-Gluon Plasma.
URL https://home.cern/science/physics/heavy-ions-and-quark\54

-gluon-plasma. Accessed on 2023-07-24.
[12] M. G. Mustafa and M. H. Thoma. Energy Loss of Heavy Quarks in the
Quark-Gluon Plasma. Elsevier, 1998. doi: 10.1016/S0370-2693(98)00452-2.
Physics Letters B, Volume 428, Issues 3–4, Pages 234-238.
[13] T. Matsui and H. Satz. J/ Suppression by Quark-Gluon Plasma Formation. Elsevier, 1986. doi: 10.1016/0370-2693(86)91404-8. Physics Letters B, Volume
178, Issues 4, Pages 416-422.
[14] S. Chatrchyan and et al. (CMS Collaboration). Observation of Sequential
Suppression in PbPb Collisions. American Physical Society, 2012. doi: 10.
1103/PhysRevLett.109.222301. Physical Review Letters, Volume 109, Issue
22.
[15] S. Cao and et al. Determination of Heavy-Quark Fragmentation Functions andtheir Uncertainties. Springer, 2016. doi: 10.1007/JHEP01(2016)006. Journalof High Energy Physics, Volume 2016, Issue 1.
[16] N. Brambilla and et al. (Quarkonium Working Group). Heavy Quarkonium:
Progress, Puzzles, and Opportunities. Springer, 2011. doi: 10.1140/epjc/
s10052-010-1534-9. The European Physical Journal C, Volume 71, Issue 4.
[17] The ALICE Collaboration. Upgrade of the ALICE Experiment: Addendum
to the Letter of Intent. CERN, 2013. URL https://cds.cern.ch/record/1582252. ALICE-UG-003 CERN-LHCC-2013-014 / LHCC-I022-ADD-1.
[18] S. Agostinelli and et al. Geant4 - A Simulation Toolkit, volume 506 of Nuclear
Instruments and Methods in Physics Research A. Elsevier, 2003. doi: 10.1016/
S0168-9002(03)01368-8.
[19] Christian Bierlich, Smita Chakraborty, Nishita Desai, Leif Gellersen, Ilkka
Helenius, Philip Ilten, Leif Lönnblad, Stephen Mrenna, Stefan Prestel, Christian T. Preuss, Torbjörn Sjöstrand, Peter Skands, Marius Utheim, and Rob
Verheyen. A comprehensive guide to the physics and usage of PYTHIA 8.3.
2022. URL https://pythia.org/documentation/. LU-TP 22-16,
MCNET-22-04.
[20] Ren Ejima. Development of Muon Reconstruction Method Based on Machine
Learning for ALICE Run 3 and Improvement of Performance for Verificationof Origin of Hadronic Mass. 2023.
[21] The ALICE Collaboration. Technical Design Report for the Upgrade of theOnline–Offline Computing System. 2015. ALICE-TDR-19, CERN-LHCC2015-006, June 2, 2015.

55

Acknowledgement
First of all, I would like to express my gratitude to everyone who was involved in
my research. I would like to thank Professor Kenta Shigaki for his efforts in guidingme through useful discussions and advice for the research. I have been given many
opportunities to participate in advanced scientific research, workshops, and conferences. In particular, my scientific presentations have been very beneficial learning
experiences. I would also like to thank Associate Professor Yorito Yamaguchi andAssistant Professor Satoshi Yano for their constructive comments and guidance.
You have been helping me a lot through the simulations and analysis framework,
despite the time constraint. I am eager to delve further into this area, given theopportunity. I extend my thanks to Professor Kensuke Homma, mainly for the interesting laboratory lessons and insightful comments in the meetings. Associate
Professor Takahiro Miyoshi also provided assistance during the courses, although
I don’t usually get the chance to talk with you. Researcher Masanori Ogino also
helped me with managing the computer programming. I would also like to thank all
the other Japanese students who have given me a lot of help, including physics discussions and support with my research work. Without their support, I would havebeen lost. Thank you, everyone, for giving me great memories while I am in Japan.
Thank you also to JICA for their sponsorship and other support they have provided me. I would like to express my gratitude to my family for their moral andspiritual support. Finally, I offer my thanks to God for granting me the chance tospend my time gaining knowledge in Japan, despite the fleeting nature of life.

56