Selection of showering events and background suppression in … Ibnsalih... · 1.1.2 Mechanism of...

Alma Mater Studiorum · Universita diBologna

FACOLTA DI SCIENZE MATEMATICHE, FISICHE E NATURALI

Scuola di Scienze

Dipartimento di Fisica e Astronomia

Corso di Laurea Magistrale in Fisica

Selection of showering events andbackground suppression in ANTARES:comparison between the effects using

two different Monte Carlo version

Relatore:Prof. Maurizio SpurioCorrelatore:Dr. Federico Versari

Presentata da:Walid Idrissi Ibnsalih

Anno Accademico 2017/18

2

Abstract

Al momento ANTARES e il piu grande telescopio di neutrini sottomarino ed

e situato nel Mar Meditteraneo, circa 40 km a largo di Tolone, Francia, ad

una profondita di 2450 m in fondo al mare. Lo scopo principale di ANTARES

e quello di poter osservare neutrini di alta energia riconducibili a sorgenti as-

trofisiche. Un fondo irriducibile del rivelatore e rappresentato dai muoni atmos-

ferici, prodotti dalle interazioni dei raggi cosmici con i nuclei dell’atmosfera. La

collaborazione ANTARES fa largo uso di simulazioni Monte Carlo e recente-

mente e stata rilasciata una nuova versione della simulazione che tiene conto di

effetti di invecchiamento del rivelatore al fine di migliorare l’accordo tra la sim-

ulazione ed i dati. In questa tesi, utilizzando eventi di neutrini di tipo sciame, si

sono confrontate la vecchia e la nuova simulazione Monte Carlo, in particolare

ci si e concentrati sulla reiezione del fondo dovuto ai muoni atmosferici.

Contents

1 Atmospheric neutrinos 7

1.1 Cosmic Rays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.1.1 Energy spectrum . . . . . . . . . . . . . . . . . . . . . . . 8

1.1.2 Mechanism of acceleration . . . . . . . . . . . . . . . . . . 10

1.2 Atmospheric shower . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.2.1 Atmospheric neutrinos flux . . . . . . . . . . . . . . . . . 13

1.2.2 Neutrino interaction . . . . . . . . . . . . . . . . . . . . . 16

1.2.3 Neutrino oscillation . . . . . . . . . . . . . . . . . . . . . 17

2 Neutrino telescope and ANTARES 21

2.1 Cherenkov effect . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.2 Topology of events . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.2.1 Track events . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.2.2 Shower event . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.2.3 Double-bang event . . . . . . . . . . . . . . . . . . . . . . 25

2.3 ANTARES experiment . . . . . . . . . . . . . . . . . . . . . . . . 27

2.3.1 Junction box . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.3.2 ANTARES line component . . . . . . . . . . . . . . . . . 28

2.3.3 Data acquisition . . . . . . . . . . . . . . . . . . . . . . . 31

2.4 Sea water properties . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.4.1 Propagation light . . . . . . . . . . . . . . . . . . . . . . . 34

2.4.2 Optical background . . . . . . . . . . . . . . . . . . . . . 36

2.4.3 Biofouling and sedimentation . . . . . . . . . . . . . . . . 37

2.5 Effective Area Aeffν . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3 Reconstruction algorithms 41

3.1 TANTRA algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.1.1 Hit selection . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.1.2 Position reconstruction . . . . . . . . . . . . . . . . . . . 42

3.1.3 Direction reconstruction and energy estimator . . . . . . . 43

3.2 Track algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3

4 CONTENTS

3.2.1 AAfit reconstruction . . . . . . . . . . . . . . . . . . . . . 44

3.3 Gridfit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4 Monte Carlo ANTARES 51

4.1 Detector can . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

4.2 Generation of physics events . . . . . . . . . . . . . . . . . . . . . 52

4.2.1 Simulation of atmospheric muons . . . . . . . . . . . . . . 52

4.2.2 Simulation of neutrinos . . . . . . . . . . . . . . . . . . . 53

4.3 Light emission and propagation . . . . . . . . . . . . . . . . . . . 56

4.4 Simulation of data acquisition . . . . . . . . . . . . . . . . . . . . 58

4.5 Run-by-run approach . . . . . . . . . . . . . . . . . . . . . . . . . 58

5 Monte Carlo v3 and v4 63

5.1 Event selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

5.2 Event selection rbr v3 . . . . . . . . . . . . . . . . . . . . . . . . 65

5.3 Event selection rbr v4 . . . . . . . . . . . . . . . . . . . . . . . . 66

5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Introduction

This thesis is carried out with the ANTARES collaboration, the largest neu-

trino telescope in the Northern hemisphere. The ANTARES (acronym for As-

tronomy with a Neutrino Telescope and Abyss environmental RESearch) neu-

trino telescope is a three-dimensional array of photomultipliers distributed over

12 lines, installed at a depth of 2475 meters in Meditterean sea. The main

purpose of this Collaboration is to observe the neutrinos emitted by the as-

trophysical objects, like AGN, GRB, and supernova remnants. Therefore, the

study of these neutrinos can solve important questions: the sources or the mech-

anism of acceleration of cosmic rays.

High energy neutrinos interact (via weak interaction) with one of the nucleons

of the medium would produce charged particle. Cherenkov photons can then

be detected by a lattice of photomultipliers. An irreducible background for the

detector is represented by atmospheric muons, produced by the interactions be-

tween the cosmic rays and the nucleus of the atmosphere.

In order to reduce the events of atmospheric muons, studies with Monte Carlo

simulations have been performed. The main purpose of this thesis is to compare

the efficiency in rejecting muon events between the old simulation version and

the one currently produced. This thesis is divided as follows. In Chapter 1 we

introduced the theoretical aspects of the subject, such as cosmic rays and in

particular atmospheric neutrinos. In Chapter 2 the ANTARES neutrino tele-

scope is presented. A description of the reconstruction algorithms for the shower

and track events are presented in Chapter 3. Finally in Chapter 4 there is a

description of the Monte Carlo simulations of ANTARES, and the last Chapter

5 reports the results made by comparing two different versions of Monte Carlo.

5

6 CONTENTS

Chapter 1

Atmospheric neutrinos

Atmospheric neutrinos are produced by the interaction of primary cosmic rays

with the nuclei of the Earth’s atmosphere. Since they are the main argument

of this thesis a short theoretical digression in this chapter is illustrated, starting

from cosmic rays1.

1.1 Cosmic Rays

Cosmic Rays (CRs) are high-energy particles accelerated by astrophysical sources.

They are generally divided into two categories: primary CRs, i. e. the particles

that reach the Earth’s surface, and the secondary CRs, i. e. the product of the

interaction between primary CRs and the Earth’s atmosphere. The primary

ones are mainly composed of protons and ionized light nuclei. In particular, the

abundance is divided into the following percentages: 85% protons, 14% alphe

nuclei, 1% electrons and the rest are nuclei with mass greater than that of he-

lium. The chemical composition in these cosmic rays is practically identical to

that within the Solar System, except for elements such as Li-Be-B and Sc-Ti-V-

Cr-Mn, see figure 1.1. In fact Li-Be-B are relatively more abundant in CRs than

in the Solar System by several orders of magnitude, because these element act

as catalysts for nuclear reactions within the stars (for the first group of elements

is the CNO cycle). So the enhancement in their relative abundance in the CRs

for the first group is given from the collisions, spallation processes (fragmetation

process), between element such C,N,O and the interstellar medium (ISM).

1Since in literature is used the acronym CR, also in this thesis is reported.

7

8 CHAPTER 1. ATMOSPHERIC NEUTRINOS

Figure 1.1: The imagine represent the relative (to Si = 100) abundance as

function of the number atomic Z [3]. There is evident difference about the

abundance between the CRs (bue line) and Solar System (red line) for the two

groups of element(Li-Be-B and Sc-Ti-V-Cr-Mn).

1.1.1 Energy spectrum

The energy of the CRs can be extend from the MeV to the 1020 eV. Below 1014

eV are possible to detect CRs with satellite and balloon experiments, instead at

higher energies only indirect measurements are available because the CRs flux

is too low.

The energy spectrum above 109 eV can be represented according to the following

power law:

dN

dE= k · E−α (m−2sr−1s−1GeV −1) (1.1)

where k is a normalization factor and α is the spectral index. In the represen-

tation on a double logarithmic scale it becomes a straight line with an angular

coefficient of α. Depending on the energy region considered, as shown in figure

1.1. COSMIC RAYS 9

1.2, the slope of the energy spectrum changes.

Figure 1.2: The differential energy spectrum of CRs, in units

m−2sr−1s−1GeV −1, from 109 eV to 1020 eV [12].

The flux can be divided in different region:

• For energies below 1015 eV the spectral index α is approximately 2.7;

• At energy 3 · 1015 eV, α ' 3.1,this region is so-called knee. The accel-

eration mechanism until this energy it can be explain with the Fermi’s

mechanism [6], see § 1.1.2. Many hypothesis support that CRs with a

energy until to 1015 eV are originated within the galaxy;

• For energies above to 3 ·1019 eV the value of α becomes ∼ 2.7 again. This

region is called ankle;

• A cut-off at 6 · 1019 eV is present, as expected to the so-called GZK phe-

nomenon (Greisen Zatsepin Kuz’min). This phenomenon describes a inter-

cation between CRs and Cosmic Microwave Background (CMB) photons


[7]:

p+ γCMB → ∆+ → π+ + n (1.2)

CRs at top of the atmosphere are distrubeted isotropically, because the

charged particles are deflected by the Galactic magnetic field (∼ 3 µG) and

extra-galactic (∼ nG). Only very high energy particle comes directly from origin

source.

The motion of a charged particle in a magnetic field can be described with

Larmor radius [14]:

RL =mv⊥|q|B

(1.3)

As can be seen from the equation, the radius is linearly dependent on the

energy of the particle. The low energy or higher charge CRs are spread through-

out in the Galaxy for a long time. Therefore very high energy (above 1018 eV)

CRs are bent with a radius compatible with the height of the Milky Way (∼200

pc). This model is called the leaky box model.

1.1.2 Mechanism of acceleration

As mentioned previously, Enrico Fermi [4] firstly suggested the CRs mechanism

acceleration through iterative scattering processes. In this model low-energy

CRs, trapped in a magnetic field inhomogeneities, can reach high energies af-

ter a large number of interaction with shock waves. These shock waves can

produced in many extreme enviorements such as supernova remnants or black

holes.

In particular there are two models for CRs acceleration. The first model as

referred to as ”Fermi second order acceleration”, whereas the second is of-

ten called ”Fermi first order acceleration”. In the first work Fermi assumed

that the charged particles are accelerated by elastic scattering with turbulence

structures or clouds of gas moving with a characteristic velocity. The gain of en-

ergy for the particles in this model is proportional to β2, where β is the velocity

of the cloud in units of the velocity of light in vacuum [5]:

<∆E

E>' (4/3)β2 (1.4)

In the second work, where reach a higher efficiency, the particle is accelerated

if encouter a shock front or is moving between two clouds. In this case the gain

energy is proportional to β.

A possible envoriment for Fermi first order mechanism acceleration (fig. 1.3)

is a supernova remnants, where after explosion a shock front is built and the

1.1. COSMIC RAYS 11

particles can be accereleted. The acceleration mechanism due to the supernova

explosion can explain some features of the energy spectrum of CRs, for example

the change slope at knee of the CRs spectrum energy.

Figure 1.3: In the left part is depicted the second order Fermi acceleration

model. Therefore in right part is depicted the first order Fermi acceleration

model, where the plane shock wave is labelled with VS [29].

The precence of the knee can be explain with the Z dependence (charge of

nuclei) of the maximum energy achievable for the particles [12]:

Emax ≈ 300 · Z TeV (1.5)

As shown in figure 1.4, the chemical composition becomes heavier for value

above the knee. The protons can be accelerated to up ≈ 1015 − 1016 eV.

The energy spectrum of particles accelerated via Fermi mechanisms is de-

scribed for all types of charged particles by an unbroken power law with spectral

index α ≈ 2. This model can describe the acceleration mechanism for CRs be-

low the knee, but fails when to describe the CRs energy spectrum above the

knee. So additional model can be taken account for galactic source:


Figure 1.4: The interpretation of the CRs knee as due the correlation between

the maximum energy achievable for the particles and the nuclear charge Z. The

flux for all nuclear species decreas until to a given cut-off, that depends to the

nuclear charge. The plot represents the behavior for proton, Si (Z=14) and iron

(Z=26) [12].

• Pulsar is a neutron star with a strong magnetic field (1011−1012 Gauss).

Indeed, the rotation axis doesn’t coincide with the direction of magnetic

field of the NS. The rotation of these magnetic field around the axis of

rotation produce a strong electric fields through Faraday’s law. These

fields can accelerate the charged particles up to ≈ 1018 eV;

• Microquasars are binary systems composed by a normal star and a com-

pact object as black hole, neutron star or pulsar. These system can emit

relativistic jets or elettromagnetic radiation. The precence of these jets

produce a strong elettromagnetic fields, so charged particles according to

the hypotheses can be accelerated until a energy ≈ 1016 eV;

A possible extra-galactic source for acceleration of CRs are AGNs (Active

Galactic Nuclei). AGNs are very powerful source of elettromagnetic radiation

and jets matter. Often a supermassive black hole in the center of these galaxies

is present. Some models hypothesizes that AGNs are possible candidate to

1.2. ATMOSPHERIC SHOWER 13

accelerate CRs to the ultra-high energy.

1.2 Atmospheric shower

When a primary CRs enter inside the Earth’s atmosphere interact with the nu-

clei and produce a large cascade of subatomic particles, the so-called air shower.

These particles are referred as secondary CRs.

The interaction between the primary CRs and the nucleon produces firstly

hadronic particle such as kaons and pions:

p+N → π±, π0,K±,K0, ...... (1.6)

Charged pions and kaons can either initiate further interactions or decay,

and the competition between these process is function of the energy.

The charged kaons and pions produce leptonic component like neutrino and

muon via decay, while neutral pions produce a elettromagnetic cascade via de-

cay π0 → γγ. Therefore, the atmospheric shower is composed of a hadronic,

electromagnetic and muonic component.

1.2.1 Atmospheric neutrinos flux

Up to ≈ 100 TeV, muons and neutrinos in the atmosphere are produced mainly

by decays of charged pions and kaons:

π+(K+)→ µ+ν (1.7)

π−(K−)→ µ−ν (1.8)

Analytically, the differential flux of muons at sea level can be derived [12]:

Φµ(E) = KE−α

(Aπ

1 + (BπEεπ )cosθ+

AK

1 + (BKEεK)cosθ

)(1.9)

Where Aπ and AK depend on the ratio of muons produced by kaons and

muons and can be derived from Monte Carlo computations. The quantity εicorresponds to the energy at which the hadron interaction and decay lengths

are equal. Below these energies hadrons are more likely to decay, while at

higher energies, due to relativistic reasons, the probability to intercact with the

atmosphere is greater than the decay process. The characteristic decay constant

(εi) for pions and kaons is [12]:

επ ' 115 GeV

εK ' 850 GeV (1.10)


Figure 1.5: The atmospheric muon flux at ground in function of the muon energy

[43].

Due to the kinematics of two-body decay, neutrinos and muons have a different

energy spectrum. The muons obtain more energy than the neutrinos, for this

reasons the spectrum of neutrinos is given with a expression similar to 1.9, but

shifted toward for lower energies. The neutrino atmospheric flux can be write:

Φν(Eν) = AνE−α

(1

1 + (aEνεπ )cosθ+

B

1 + ( bEνεK )cosθ

)(1.11)

This flux, with units expressed in (m−2sr−1s−1GeV −1), is called ”conven-

tional atmospheric neutrino flux”. The scale factor Aν , the balance factor B,

which depends on the ratio of muons produced by kaons and pions, and the a,

b coefficients are parameters which can be derived from Monte Carlo compu-

tation. An analytical description of the neutrino spectrum above 100 GeV is


provided by the Volkova parametrisation [39], and also provided by the Barr

et al.[42] and Honda et al. calculations [41]. The comparison between these

models is represented in fig. 1.6.

At low energies the uncertainties of the atmospheric neutrino flux are dominated

by uncertainties of hadronic interaction processes, while the uncertainties in the

calculations arise from the limited knowledge of the composition of the primary

CRs. In high-energy approximation of neutrinos, (Eν >> εi), the flux can be

approximated with:

Φν(E) = A′

ν · E−γ (1.12)

with spectral index that can be rewritten as:

γ = α+ 1 (1.13)

Also the decay of muons produces neutrinos in atmosphere :

µ± → e±νeνµ (1.14)

At very high energies, another neutrino production mechanism is possible.

The charmed particles, produced by the interaction of the primary CRs with

the atmospheric nuclei, also decay to neutrinos.

The lifetime of charmed particles is approximately 5 to 6 orders of magnitude

smaller than pions and kaons, and for this reason there is no competition be-

tween the loss energy with collision and the decays. The cross section to product

charmed particles in a collision proton-nucleon is small, therefore this channel to

produce neutrinos is expected important for energies above 100 TeV. The neu-

trino flux that also includes this phenomenon is called prompt neutrino flux.

Two fundamental propetiers for the flux can be deduced. Given the decay chain,

the first is:

Φνµ ∼ 2Φνe (1.15)

As the muon decay probability in the atmosphere decreases with increasing Eµ,

this condition (1.15) is true at low energies. Moreover the fluxes of electronic

neutrino and muonic neutrino are up-down symmetric (considering the Zenith

angle θ):

Φνi(Eν , θ) = Φνi(Eν , π − θ) i = e, ν (1.16)

This prediction is consequence of the isostropy CRs primary and the quasi-exact

spherical symmetry of the Earth. These assumptions are correct but don’t take

into account the oscillation of neutrinos, phenomenon predicted firstly by Bruno

Pontecorvo [38].


Figure 1.6: The energy spectrum of νe,νe,νµ and νµ calculated from the Honda,

Bartol and Battistoni model [41].

1.2.2 Neutrino interaction

Neutrinos can interact with matter only through weak interaction. The weak

process involve the exchange of gauge bosons such as W± and Z0. This process

have a small cross section, so for this reason neutrinos are very penetrate par-

ticles. For example neutrinos with an energy 1 TeV have an interaction length

of 250 · 109g/cm2.

Considering a nucleon N, neutrinos can perform two types of processes.

νl +N → l± +X (1.17)

With l indicating the leptonic flavour (e, µ and τ). These process are called

charged current weak intercation (CC), and W± bosons are involved. In the

final state are produced a lepton with the leptonic flavour associated and a


hadronic shower (X).

The second type that can occur is neutral current weak intercation (NC), where

the gauge boson Z0 is involved:

νl +N → νl +X (1.18)

The differential cross-section for CC intercation can be written as:

d2σ

dxdy=

2GFMEνπ

(M2W

Q2 +M2W

)[xf(x,Q2) + xf(x,Q2)(1− y)2

](1.19)

The variable x is called Bjorken’s variable, which represents the fraction of

four-momentum transported by the parton, and y, which represents the fraction

of energy transferred by the neutrino to the lepton produced:

y =Eνl − ElEν

(1.20)

The term Q2 represents the fraction of energy transferred from the neutrino

to the charged lepton.

In the formula the functions f and f , which are the distributions of quarks and

antiquark within the nucleon, are present. Therefore, in order to have a correct

calculation of the cross sections, it is necessary to know these distributions.

Same calculation are similar to antineutrinos.

The neutral processes, however, can be written as:

d2σ

dxdy=

2GFMEνπ

(M2Z

Q2 +M2Z

)[xf0(x,Q2) + xf0(x,Q2)(1− y)2

](1.21)

The cross-section (represented in the figure 1.7) increases as the energy increases,

this means that the interaction length of neutrinos decreases with increasing

energy.

1.2.3 Neutrino oscillation

As has already been mentioned, neutrinos are subject to so-called neutrino

oscillation. The Standard Model predicts that neutrinos are neutral and mass-

less particles. According to the model, the neutrinos cannot change the leptonic

flavour, identified as Le,Lµ and Lτ . If the neutrinos are massive as suggested

by B.Pontecorvo, the change of the leptonic number becomes possible. This

is due because the ”weak flavor eigen states”(νl) are different from the masses

autostates νi, but a combination of them: νeνµντ

=

U11 U12 U13

U21 U22 U23

U31 U32 U33

ν1

ν2

ν3

(1.22)


Figure 1.7: The cross section of the intercation for all type of neutrino in function

with the energy [44].

The combination can be written:

|νl〉 =

+3∑i=0

Ul,i|νi〉 (1.23)

With Ul,i is the elements of the mixing matrix. It can be possible to determine

the probability that along the way the neutrino will change the flavour from l1to l2:

P1,2 = sin2(2θ1,2)sin2(1.27L

E∆m2) (1.24)

The various terms describe:

• θ1,2 is the mixing angle between neutrinos;

• ∆m is the difference between the neutrino masses;


• L is the length of the path from where the neutrino is produced to the

observation point.

The energy dependence of the neutrino should be noted.

Chapter 2

Neutrino telescope and

ANTARES

Many theories suppose that high-energy neutrinos are emitted in violent events

taking place in many astrophysical objects. The neutrinos originating from in-

teractions of CRs with the surrounding medium come from their origin source

without any deflection from magnetic fields Galaxy.

The possible detection of high energy neutrinos is limited by the fact that the

expected fluxes and the neutrino interaction cross-sections are very low. Very

large detectors are needed, ranging up to a cubic km of instrumented volume.

As proposed by M.A. Markov, it can be possible the use of large volumes of nat-

ural water. He proposed: ”to install detectors deep in a lake or in the sea and

to determine the direction of the charged particles with the help of Cherenkov

radiation” [1]. The main idea of detecting neutrinos with a neutrino telescope is

to observe downward through the Earth, because all other particles (as muon)

cannot penetrate it. Therefore, neutrinos that have crossed the Earth can in-

teract with the detector medium or the surroundings, and can produce the

corresponding charged lepton, see § 2.2. A 3D-array of PMTs allows the detec-

tion of the Cherenkov light released by ultra-relativistic particles crossing the

detector. From the Markov’s idea nowadays the km3 detectors are operating.

2.1 Cherenkov effect

When a charged particle traversing the medium, with a determineted refractive

index n, faster than the velocity of light in that medium, a electromagnetic

radiation is emitted. This radiation is called as ”Cherenkov radiation”.

The threshold for Cherenkov-effect is v > cn , corresponds to a threshold in the

21

22 CHAPTER 2. NEUTRINO TELESCOPE AND ANTARES

γ factor of the particle [19]:

γth =n√

n2 + 1(2.1)

This happens because the particle tends to polarize the medium, so the

atoms along the track become a electric dipoles. Varition time of these depoles

mean emission of light radiation [8]. An illustration of the phenomenon is given

in figure 2.1.

Figure 2.1: A simple illustration of Cherenkov emission. If the velocity of the

particle exceedes the velocity of light a coherent radiation is produced [20]

With simple geometric considerations this radiation is emitted at a angle

respectively to the track particle [2]:

cosθc =1

βn(2.2)

where θc is the Cherenkov angle, and β is the ratio between the velocity of

particle (v) and c, velocity of the light in vacuum. In water, with a refractive

index of n ∼ 1.33, considering a particle with relativistic energy, the angle of

emission is about 43◦.

The number of photons produced per unit of path and per unit of wavelength

is [2]:

2.2. TOPOLOGY OF EVENTS 23

d2N

dxdλ=

2παZ2

λ2sin2θc (2.3)

Detection Cherenkov light allows the trajectory of the particle to be recon-

structed.

2.2 Topology of events

A breif treatment of the processes of neutrinos that interact with matter is

proposed in paragraph § 1.2.2. There are three event topologies (as shown in

figure 2.2) in a neutrino telescope:

• track event;

• shower event;

• double-bang event by tau neutrino.

Figure 2.2: Event signature for all flavour of neutrinos: a) represent a charged

current interaction of νµ, b) double-bang event, by a neutrino ντ , c) shower

event from a CC interaction for νe and d) a NC interaction produced by all

flavours neutrino [13].

2.2.1 Track events

A high-energy muon can be produced by muon neutrino after CC interactions

with matter. The direction of the µ is highly correlated with the neutrino arrival


direction and the average θνµ angle (fig. 2.3) between the incoming neutrino

and the induced muon is:

θνµ '0.7◦√Eν(TeV )

(2.4)

Using this event topology it can be possible to find the direction of neutrinos.

A muon can lose energy, in addition to the Cherenkov effect, via bremsstralung,

ionization and pair production. The energy loss per unit path length of the

muon is described with the following relation [14]:

dE

dX(Eµ) = α(Eµ) + β(Eµ) · Eµ (2.5)

where α(Eµ) is the parameter that describes the energy loss due to ionization

alone and the term β(Eµ) describes the radiative processes.

A critical energy Ec, depending on the traversed material, exists: above that

the radiative losses become larger than ionisation losses. In water this energy

is ∼ 500 GeV.

Defining Reff as the distance after which a muon of initial energy Eµ is still

above the energy Ethµ to be observed by the apparatus. This largely increases

the effective volume of the detector, neutrino interactions can happen far away

from the instrumented volume. Figure 2.4 shows the effective range (Reff ) in

the water.

However for the majority of high energy events, the interaction vertex is

far outside the detector. As a consequence only part of the muon track is

directly observed in the detector, this limits the capabilities of neutrino energy

reconstruction for track-like events.

2.2.2 Shower event

When a neutrino νe interact via CC process an electron or positron is created,

that produces a electromagnetic shower (see figure 2.2). A high energy electron

can radiate a photon via Bremsstrahlung. Consequent an electron-positron pair

is produced via pair production that itself will radiate via Bremsstrahlung again

and hence evolve a cascade of electrons, positrons and photons. If the energy

particles is above the thershold condition for Cherenkov radiation, the light is

emitted.

The longitudinal development of an electromagnetic shower is well understood

(fig. 2.5). For a radiation length (the distance which the electron loss ∼ e−1

energy only for radiation) ∼ 36 g/cm−2 in water, the maximum shower lies

2.2. TOPOLOGY OF EVENTS 25

Figure 2.3: The plot shows the average angle in function of neutrino’s energy.

For 1 TeV of energy neutrino the θνµ is approximately 0.7◦ [21].

between 0.6 m for 1 GeV and 7 m for 100 PeV. Therefore, given the size of

the detector, the showers are very compact so can be approximated like a point

source.

Unlike track events, shower events are characterized by worse angular resolution;

but the energy resolution is better compared to track events.

Hadronic showers are produced when a neutrino (of every flavour) interacts via

NC or CC.

2.2.3 Double-bang event

When a τ neutrino interacts via CC interaction, a τ lepton and hadronic shower

(see figure 2.2) are produced. Tauons are unstable particles, have a lifetime

about 2.9·10−13 s [24], and can decay producing various particles. Three relevant

decay scenarios are possible [21]:


Figure 2.4: The effective range of muon in function of the energy. Different line

is relate to the different energy thersholds (from 1 to 106 GeV) [23].

τ → ν + ν + µ (17.4%) (2.6)

τ → ν + ν + e (17.9%) (2.7)

τ → ν +X (64.7%) (2.8)

The interaction of a τ neutrino with matter is associated with a double bang

signature (see Fig. 2.2). The first bang is defined by a hadronic shower, while

the second bang is caused by the short tauon lifetime. For high energy tauons,

due to the relativistic reason, is possible to observe a track between the two

bangs.

Below 1 PeV the hadronic and decay vertex are very closely, so is complicated

to discriminate them. If the τ decay starts or ends out of the instrumentad

volume, the event will have one shower less than the double-bang event. This

case is referred as lollipop event.

2.3. ANTARES EXPERIMENT 27

Figure 2.5: The plot shows the longitudinal distance for all topologies events in

function of the energy [25].

2.3 ANTARES experiment

ANTARES (acronym for Astronomy with a Neutrino Telescope and Abyss envi-

ronmental RESearch) is a neutrino telescope installed at a depth of 2475 meters

in Meditterean sea, 40 km from La Seyne-sur-Mer in the Gulf of Lion, Southern

France. The construction of the telescope started in 2006 with the first detec-

tion line. Between December 2007 and May 2008, it was then completed. Is

at present the largest neutrino telescope in the Northern hemisphere and the

largest under-water neutrino detector.

The telescope consists a three-dimensional array of 885 optical modules

(OMs) arranged in triplets (so called storey) distributed on 12 lines with a

vertical spacing of 14.5 m between (see figure 2.6). The lines are anchored on

the seabed and are held in tension by buoys located on the surface.

The total length of each line is 450 m and the separation between the lines

ranges from 60 to 75 m [12]. All lines are connected to a Junction Box on the

sea-bed, which is then connected to the shore station with a 42 km-long elec-

trooptical cable (the Main Electro-Optical Cable, MEOC). Besides the main

electro-optical cable provides the electrical power to the detector. In the shore

station the data are treated by a PC farm where they are filtered and then sent


Figure 2.6: Schematic view of the ANTARES detector [26].

via the fibre optic network to be stored remotely at a computer centre in Lyon

[26].

2.3.1 Junction box

The Junction box (JB) is a pressure resistant titanium container mounted to the

JB Frame (see figure 2.7), so called JBF, and distributes the power supply,clock

signal and data trasmission to the 13 lines via interlink cables. The structure

of JB is based on a 1 m diameter titanium pressure sphere (fig. 2.8), whose

hemispheres are separated by a central titanium cylinder through which all

power and data connections pass to the exterior [26].

2.3.2 ANTARES line component

From bottom to top a line of ANTARES is composed:

• Bottom String Socket (BSS) is the connection between the line and a

dead weight which fixes the whole unit on the seabed. This module con-


Figure 2.7: This imagine shows the Junction box container and its frame [26].

tains a sub-module to control the string (SCM, String Control Module),

a component (SPM, string power Module) to supplement the power of all

the tools in the line and an acoustic signal emitter. The acoustic system

is used for both sending and receiving acoustic signals so that possible to

monitor the position of the line. The SCM contains the electronics capable

of transmitting data with the control room;

• Storey (fig. 2.9) is a titanium frame that carries three Optical Module

(OMs), see fig. 2.11, and many device for the positioning and calibrations.

The Optical Module consists of a pressure resistant glass sphere with 43

cm in diameter and 15 mm thick. Inside an Optical Module there is a

spherical PMT with a diameter of 25 cm, able to reach a gain of about

107 with a nominal voltage of 1760 Volts. A PMT can take a quantum

efficiency of 25 % in the wavelength range between 350 nm and 600 nm.

A cage of µ-metal is used to shield the PMT from the Earth’s magnetic

field, which modifies the transit-time of photoelectrons. The three OMs

are mounted on an OMF (Optical Module Frame), made of titanium,


Figure 2.8: The Junction box [27]

and are orientated 45◦ downwards, to increase the detection of Cherenkov

light directed upwards. Additional device are present:

- LCM, Local Control Module, is the main electronic container on

each storey, where the PMT output is digitized by the Analog Ring Sam-

pler (ARS), see §2.3.3;

- Hydrophones The sea currents, typically with a 5 cm/s, can change

the lines their position by a few cm. This would result in a poor recon-

struction of the event. For this reason, an acoustic system is configured,

which uses the emitter placed in each BBS. The distance between the hy-

drophones, located somewhere on the line, and the BBS is obtained by

the speed of sound in the water. Therefore, through a triangulation sys-

tem, the position of the OMs can be obtained any time (about every two

minutes). At storey 1, 8, 14, 20 and 25 a receiver hydrophone is mounted

for acoustic positioning;

- Optical Beacons are LED Optical Beacons (LOB) placed for each

line at storey 2, 9, 15 and 21 in order to illuminate the OMs located above


Figure 2.9: A picture of ANTARES storey. The LED-beacon (blue) and the

hydrophone (ochre) for acoustic positioning are visible [27].

on the same line for time calibration issues.

• Buoy: Each line is pulled taught by a top buoy, made of composite syn-

tactic foam is mounted and with a density 0.5 g/cm3.

2.3.3 Data acquisition

The main purpose of the DAQ (data acquisition) is to digitize the analog input

signal from PMTs, thus obtaining useful information for the reconstruction of

the event (arrival time, signal amplitude, etc.). DAQ system is split in three

main part [20] [28]:

• digitize the analogue PMT signal;

• process the data stream online (on shore) and generate physics events by

applying triggers;

• store the information in an appropriate data format.


Figure 2.10: The image shows a series of storeys in the laboratory, before the

deployment of the detector at sea [27].

The first part is devolped by two analogue ring samplers ARS, inside the

LCM. These work according to two precise parameters: the threshold charge

fixed at 0.3 photoelectrons, to eliminate effects due to dark current, and the

duration of the integration window ∼ 30-35 ns.

If there is a signal above the threshold ARS collects data for 30 ns, how-

ever follow a dead time of 200 ns. To prevent events losses, after a token

transmission (∼ 10-15 ns), the second ARS communicates with the PMT and

takes data, as shown in fig. 2.12. This communication protocol is called the

Token Ring Protocol.

The data is sent to the BBS, which is connected to the JB, so via the MEOC the

data are transferred to the central control. The ANTARES has tree different


Figure 2.11: The figure represents an optical module of Antares. Within this

glass ball is contained the PMT [27]

trigger:

• L0 (level zero): charge collection is more than the threshold, often is fixed

at 0.3 photoelectrons;

• L1 (First level trigger): hits with a large charge is identificated;

• L2 (Second level trigger): is a trigger logic algorithm that work on the L1

hits, after that the data are stored.

To reduce the amount of data, only hits above a threshold of 0.3 photoelec-

trons are taken. The corresponding L1 hits have typically a charge above 3 p.e.

or are defined as at least two L0 hits occurring on the same storey within 20 ns,

see fig. 2.13. This is due to reduce the optical background as 40K deacy and

bioluminescence. For L2 level there are two many physics algorithm:

• A 3D-directional scan logic trigger 3N, as referred five L1 hits in a time

window of 2.2 µs;


Figure 2.12: Three peaks are observed in the picture: are related to the first

ARS, its switch to the second ARS and the first ARS after the dead time (200

ns) [26].

• A cluster logic trigger T3 trigger is a collection of two L1 hits on adjacent

storeys in 80 ns or two L1 hits on adjacent storeys in 160 ns. A trigger

logic level is defined as 2T3, based on the definition of a T3 cluster of hits,

that requires at least two T3 clusters within a time window of 2.2 µs.

2.4 Sea water properties

As explain previously ANTARES telescope is installed in deep water. The

knowledge of the optical properties of sea water at the ANTARES site is ex-

tremely important to understand the response of the detector.

2.4.1 Propagation light

When the photons propagate in water two phenomena can occur: dispersion

(scattering process) and absorption. Absorption reduces the intensity of Cherenkov

light, which results in a reduction of the total charge detected by PMTs. While

scattering changes the direction of the photons, modifying the distribution of

their arrival times to PMTs. Both phenomena lead to a misreconstruction of

2.4. SEA WATER PROPERTIES 35

Figure 2.13: An example of DAQ system, only signals above the L0 threshold

and fulfilling the SPE pattern have been sent to shore [29].

events. Given a wavelength λ, the propagation of light inside the medium is

described by the coefficient absorption a(λ), scattering b(λ) and attenuation

c(λ) coefficient [14]:

c(λ) = a(λ) + b(λ) (2.9)

For each phenomena a radiation lengths can be defined:

L(λ) = i(λ)−1 i = a, b, c (2.10)

These lengths represent the path after which the initial intensity I0 of beam is

reduced to a factor 1/e respectively for absorption, scattering and attenuation.

The light intensity has the following shape, depending on lengths:

Ii(x, λ) = I0e− xLi i = a, b, c (2.11)

Where x is the optical path (in meters) of the photons.

The measure (see figure 2.14) of the attenuation length in ANTARES site for a

wavelength λ = 446 nm is:


Figure 2.14: The plot shows the absorption (blue dots) and effective scattering

(red line) length in different time. The black line is the effective scattering line

in a pure water [17].

Lc(λ) = 41± 1(stat)± 1(syst) m (2.12)

The measurement was repeated during the course of one year to understand

the time variability of water properties at the detector site.

2.4.2 Optical background

There are two main type of optical background on PMTs:

• Decays of radioactive elements;

• Bioluminescence light produced by living creatures;

40K decay is by far the dominant process among radioactive decays in sea water

[14]. Its decay channels are [15]:

40K →40 Ca+ e− + νe (89.3%) (2.13)40K + e− →40 Ar + νe + γ (10.7%) (2.14)

The electrons produced in the β-decay channel (first process) are often above

the threshold for Cherenkov light production. In the electron capture channel

2.4. SEA WATER PROPERTIES 37

(second process) fast electrons with subsequent Cherenkov light emission are

produced by Compton scattering of γ, released by the excited Ar nuclei [15].

The concentration of 40K in sea water is dependent by the salinity of the sea

water. Water salinity is almost constat in all over Mediterranean Sea, so on a

10-inch PMT the mean single rates from 40K decays is about 50 KHz.

The bioluminescence light is induced manly from two sources: glowing bacteria

Figure 2.15: The system configuration,for the second immersion, to the mea-

surement of the light transmission [16].

and flashes light produced by marine animals. This background source can be

more intense than 40K for several orders and appears as bursts in the counting

rates of PMTs.

Seasonal effects in bioluminescence can be present, for example during spring

a maximum intensity ∼ MHz single rates on PMTs can be observed. In these

period the detector may be switched-off to avoid data acquisition problems.

2.4.3 Biofouling and sedimentation

The OMs are exposed to sedimentation and adherence of bacteria (biofouling)

which reduce the light transmission through the glass sphere. These effects

on the ANTARES optical modules have been studied in [16]. The system for


measuring light transmission, shown in 2.15, is housed in two 17′′

pressure

resistant glass spheres similar to those used for the OMs.

Figure 2.16: Light transmission as a function of time from the first immersion.

Curves are labeled according to the zenith (θ) and azimuthal angle (φ) of each

photodiode. At the bottom of the figure there is the current velocity [16].

One of them was equipped with 5 photo-detectors glued to the inner surface

of the sphere at various positions which were illuminated by two blue light LEDs

contained in the second sphere.

The light flux transmitted to each photodiode is measured in order to monitor

the effect of fouling on the two glass surfaces. The measurements went on during

immersions of several months and the results were extrapolated to longer periods

of time. There were in particular two immersions. For the first immersion the

photodetectors were glued to the lower sphere at different inclinations (zenith

angles of 0◦,20◦ and 40◦). Three photodetectors were placed at 20◦ on different

meridians to test for a possible azimuthal (φ) dependence of the fouling. For

the second immersion the photodetectors were placed at zenith angles ranging

from 50◦ to 90◦,fig. 2.15.

Figure 2.16 and 2.17, shows the loss of transparency respectivaly for the first

and second immersion.

The loss of transparency (figure 2.17) in the equatorial region of the OM is

2.5. EFFECTIVE AREA AEFFν 39

Figure 2.17: Light transmission as a function of time from the second immersion.

Each curves correspond to different photodiode zenith angle [16].

about 2% after one year, then shows some saturation effect.

Considering that the OMs of ANTARES point 45◦ downward, zenith angle of

135◦, the biofouling and the sedimentation should not represent a major problem

for the experiment.

2.5 Effective Area Aeffν

The effective area can be reffered as the ANTARES detection efficiency and can

be only calculated by Monte Carlo simulations. The effective area, convoluted

with the flux of neutrino, gives the event rate [12]:

NνT

=

∫dΦνdEν

Aeffν (Eν) dEν (2.15)

In general Aeffν depends on the neutrino cross-section, on the survival probabil-

ity of neutrinos crossing the Earth and on the daughter muon (in CC process)

detection. The effective area correspond to [12]:

Aeffν = A · Pνµ(Eν , Eµthr) · ε · e

−σ(Eν)ρNAZ(θ) cm2 (2.16)


where the terms in equation rappresent:

Figure 2.18: The Aeffν in function of the neutrino energy for different ”water

model”. In up side the plot rappresent Aeffν with changing the absorption

lengths (λabs) and fixed the scattering lengths (λsca). In down side on the

contrary λsca is fixed [29]. Plot in right side are in non-log scale.

• A is the geometrical projected detector surface, expressed in cm2 units;

• ε is the fraction of muons with energy Eµthr that are detected;

• e−σ(Eν)ρNAZ(θ) describes the probability that a neutrino is absorbed when

crossing along Z(θ) in the Earth, θ is defined respect to the nadir;

• Pνµ(Eν , Eµthr) represents the probability that a neutrino with a energy Eν

produces a muon that can be observed at the detector with a residual

threshold energy Ethr [12].

In figure 2.18 is showed the effective area in function of the Eν .

Chapter 3

Reconstruction algorithms

The events, after trigger selection, are reconstructed to obtain the physics quan-

tities that are relevant for the analysis. The reconstruction algorithms for track

and shower events are presented in this chapter.

3.1 TANTRA algorithm

The TANTRA (Tino’s ANTARES Shower Reconstruction Algorithm) [31] al-

gorithm allows the reconstruction for position and energy of the shower-like

events. The reconstruction is performed principally in the following steps:

• Primaly there is a position hit selection, where is selected a set of

hits with the largest sum of associated charge compatible with a common

source of emission;

• A 4-dimensional least linear square pre-fit is performed for the shower

space-time position. After that an M-estimator fit for refinement is

accomplished;

• Shower hit selection: only hits compatible with the fitted position are

selected;

• In the last step there is the shower direction fit, which works with a

probability density funcion table based on log-likelihood minimisation to

determine the neutrino direction and energy.

3.1.1 Hit selection

The criterion selection for each pair of hits (i,j) is [31]:

|~ri − ~rj | ≥ cW · |ti − tj | (3.1)

41

42 CHAPTER 3. RECONSTRUCTION ALGORITHMS

where the terms are:

• ri is the position of the OM that hit i is observed;

• cW is the velocity of the light in the water, about ∼ 0.217288 m · s−1;

• ti is the time of hit i ;

3.1.2 Position reconstruction

After the hits selection, a sample of that is obtained. From N selected hits the

recostruction position of the shower vertex can be obtain assuming the following

quadratic system:

(~ri − ~rshower)2 = c2W · (ti − tshower)2 ∀i ∈ [1, N ] (3.2)

The ~rshower and tshower are defined as shower vertex position and time. This

system can be linearised by taking the difference between every pair of equations

i and j [31]:

(xi − xj) · xshower + (yi − yj) · yshower + (zi − zy) · zshower − c2W (ti − tj) · tshower

=1

2[x2i − x2

j + y2i − y2

j + z2i − z2

j − c2W (t2i − t2j )]

∀i, j : 1 ≤ i < j ≤ N(3.3)

The system can be represent as [31] [32]:

A~v = ~b (3.4)

where A can be written as:

A =

(x1 − x2) (y1 − y2) (z1 − z2) −cW (t1 − t2)...

......

...

(xN−1 − xN ) (yN−1 − yN ) (1zN−1 − zN ) −cW (tN−1 − tN )

(3.5)

The vector ~v::

~v =

xshoweryshowerzshower

cw · tshower

(3.6)

3.1. TANTRA ALGORITHM 43

Moreover ~b is:

~b =1

2

|~r12| − |~r2

2| − c2w(t21 − t22)...

| ~rN−12| − | ~rN 2| − c2w(t2N−1 − t2N )

(3.7)

To solve the eq. (3.4), a residual vector ~r is defined:

~r = A~v −~b (3.8)

The square of this vector has a χ2-like feature:

|~r|2 = (A~v)T (A~v) +~bT~b− 2(A~v)T~b (3.9)

A minimization to find a solution for eq.(3.4) needed [31], therefore:

~v = (ATA)−1AT~b (3.10)

An M-estimator fit with ~v is performed by minimising the following relation:

Mest =

NselectedHits∑i=1

qi ·√

1 +t2Res−i

2

(3.11)

where:

• qi is the charge of hit i ;

• tRes−i = ti - tshower - |~ri − ~rshower|/cw: the residual time of the hit i ;

3.1.3 Direction reconstruction and energy estimator

The energy and direction of the neutrino are determined by the minimisation

of the following negative log-likelihood (L) [31]:

−L =

NselectedHits∑i=1

log [Pq>0 (qi|Eν , di, φi, αi) + Pbg(qi)]

+

NunhitPMTs∑i=1

log [Pq=0 (Eν , di, φi)] (3.12)

with the varoius term indicating:


• Eν the energy of neutrino;

• di the distance between the vertex shower and PMT i , see figure 3.1;

• φi is the angle between the direction of particle and the direction of the

photons emitted;

• αi is the impact angle of photons on the PMT.

• Pq>0: the probability to observe a hit with a charge qi. This term is the

expectation value of the number of photons on a PMT, given the distance

between the shower vertex and the PMT, the photon emission angle and

photon impact angle. The number of photons expected is linear with the

neutrino’s energy. For N photons expected, the probabily to observe n

photons is described by the Poisson distribution:

P (n|N) =Nn

n!e−N (3.13)

• Pbg: the probability that the hit is caused by background, for example40K or bioluminescence;

• Pq=0: the probability for a PMT of not being hit. For N of photoelectron

expected, this term is written:

P (N) = P (q = 0|N) = e−N (3.14)

To define the PDFs, the MC simulation (see chap. 4) of showers in ANTARES

is used. This algorithm give the best performance for a shower reconstruction

ever achieved in a neutrino telescope. A resolution of the energy is about 5% -

10%, and the shower vertex is reconstructed with a error ∼ 1 meter.

3.2 Track algorithms

The track events provide a better angular relsolution than the shower events,

therefore can also be used to determine the origin of the event whether it is

a neutrino or an atmospheric muon. In this section two algorithms for track

reconstruction are described: AAfit [33] and GridFit [34].

3.2.1 AAfit reconstruction

To reconstruction the track of relativistic particle, the arrival time of a Cherenkov

photon to a PMT and the following five paramaters, see figure 3.3, are computed

[29]:

3.2. TRACK ALGORITHMS 45

Figure 3.1: Representation of the variables used in likelihood function.

• zenith angle (θ);

• azimuth angle (φ);

• the coordinate x of the point A, that is defined as the intersection with

the plan P;

• the coordinate y of the point A;

• t0 is the time when the muon cross A.

The expected time tiexp (arrival time) of a hit given muon position and

direction at an arbitrary time t0, can be written [33]:

tiexp = t0 +1

c

(Li −

ditanθc

)+

1

vg

disenθc

(3.15)

In the equation the term with Li describes the time that the muon travels

from the initial position to the point where the detected photons are emitted,

therefore the term with vg (the group velocity of light) is just the required time


Figure 3.2: Performance of TANTRA algorithm: on top left is plotted dis-

tance between the position of the neutrino interaction vertex and the recon-

structed shower position in function of energy. On top right there is the dis-

tance of the reconstructed shower position perpendicular to the neutrino axis.

On bottom left the ratio between the energy recontruction and the true en-

ergy. On bottom right the ratio between the directions of the reconstructed

shower and the MC neutrino [31].

for photons to reach the PMT. The difference between texp and the measured

arrival time (tmeas) of the photon defines the time residual [14]:

r = timeas − tiexp (3.16)

The track reconstruction method is based on a likelihood fit derived from

simulation. In this case the Probability Density Function (PDF) of the time

residuals is built under the assumption of Cherenkov emission from the track.

To obtain a optimal solution, before to improve the likelihood fit a series of

pre-fit algorithms of increasing sophistication are implemented [33] [29]:

• Linear pre-fit is a first linear fit independent of the starting point;

3.2. TRACK ALGORITHMS 47

Figure 3.3: Illustration of the variables used for AAfit recostruction [29].

• M-estimator fit: this step use the only the hits that are shorter than

distance 100 m from the fitted track (of course come from the previous

step). Moreover the hits should be on a ± 150 ns window with respect to

the expected time;

• A maximum likelihood fit with the original PDF;

• Repeation the previous step (9 time) with different starting points;

• Maximum likelihood fit with improved PDF.

The quality of the reconstruction can be estimated by the following variable:

Λ =logLNdof

+ 0.1(Ncomp − 1) (3.17)

where:

• logLNdof

is the log likelihood per degree of freedom;

• Ndof is the the number of starting points, i.e. the number of compatible

solution. For badly reconstructed events the Ncomp is 1 in average, and

for well reconstructed events it can be possible to find Ncomp = 9, in the

last case all the starting points have resulted in the same track.


3.3 Gridfit

Gridfit is a algorithm developed to optimise the track reconstruction for neutrino

with low energy [34]. The full solid angle in 500 different directions, a selection

hits compatible with a muon track performed. Consecutively a likelihood fit,

after various step, is performed. On order to separate muon from neutrino a

variable, called GridFit Ratio, is defined:

R =

∑up−going Nhits∑down−going Nhits

(3.18)

where∑up−going Nhits is the sum of the hits compatible with up-going direction

(from θ = 90◦ to θ = 0◦, with θ defined as the Zenith angle). Therefore∑down−going Nhits is the sum of all hits compatible with down-going direction.

This ratio for muon mostly is less than 1, as shown in figure 3.4, instead for

neutrinos the peak of the distribution is about 1.6 [34].

3.3. GRIDFIT 49

Figure 3.4: The distribution of Ratio for atmospheric muon, the peak distribu-

tion is about 0.6. Different plot is for different background rate: on top right is

120 kHz, on top left is 60 kHz, on bottom right is 240 kHz and on bottom left

is 180 kHz [34].

Chapter 4

Monte Carlo ANTARES

The Monte Carlo simulations are required to understand the behaviour the

detector and its physics. A software chain is schematised into three following

steps:

• Generation of physics event: particles (neutrinos and muons) are gen-

erated in proximity of the detector;

• Cherenkov light emission and propagation: particles are propagated

in the medium, and the Cherenkov light is simulated and propagated to

the OMs;

• Simulation of data acquisition: the PMT response and the optical

background is simulated. The data stream is created and the triggers are

applied.

4.1 Detector can

For the generation of the particles a sensitive volume of the detector is defined

as a cylinder containing the water region hosting the PMTs. This volume is

called can, see fig.4.1. The can defines the volume where the Cherenkov light in

the Monte Carlo is simulated. Outside this region Cherenkov photons produced

have a low probability to reach a PMT, and only particle energy losses during

propagation are considered.

51

52 CHAPTER 4. MONTE CARLO ANTARES

Figure 4.1: In figure the can is reppresents in yellow. It is anchored to the sea-

bed (in red) and containing the detector instrumented volume (in blue) [14].

4.2 Generation of physics events

4.2.1 Simulation of atmospheric muons

The most abundant signal for a neutrino telescope comes from high energy

muons that are produced in the extensive air showers via the interactions of CRs

with the nuclei of the upper atmosphere. Although the ANTARES telescope is

located at large depth under the sea, which acts as a shield, the atmospheric

muon flux is still intense at the active volume of the detector. The atmospheric

muons is an background for track reconstruction because their Cherenkov light

can mimic fake upward-going tracks. This kind of signatures can be confused

with the cosmic neutrino signal [14]. Most of them are rejected applying a

combination of geometrical and reconstruction quality cuts that select only well

reconstructed tracks moving upward. On the other side, atmospheric muons are

used to test the performance of analysis software, to monitor the detector and

to calculate the systematic uncertainties. Atmospheric muon bundles arriving

at the detector are accurately reproduced using a complete extensive air shower

simulation: the CORSIKA program [47]. With this package the interactions

4.2. GENERATION OF PHYSICS EVENTS 53

between the primary CRs and the atmospheric nuclei are simulated, than the

air shower is tracked, through the atmosphere, to the sea level.

The program offers a large choice for the input parameters, for example:

• Description of atmosphere;

• Hadronic interaction parameterisation;

• Chemical composition of the primary CR flux.

The propagation of the muons from the sea level to the detector was performed

using the MUSIC package [48] (MUon SImulation Code). This procedure gives

a precise description of atmospheric muons at the detector, but it has heavy re-

quirements in terms of CPU time and for this reason a different approch is has

been realized. In ANTARES atmospheric muon bundles are generated with

the MUPAGE software [46]. This simulation doesn’t take care of the full air

shower development, but is faster than the CORSIKA. The program uses a

parametric formulas to calculate the flux of muon bundles, taking into account

the muon multiplicity and the muon energy spectrum in a bundle [49]. The

parameterisations used in MUPAGE are extracted from a complete simulation

of events based on the results of the MACRO experiment at Gran Sasso, ex-

trapolated under the sea or under an ice layer [46].

The usage of parametric formulas allows the fast production of a large number

of Monte Carlo events, but there is the absence of flexibility in the definition of

the input parameters related to the primary composition and interaction. Even

if there are these limitations, the parametric simulation produces a reliable es-

timate of the atmospheric muon background.

4.2.2 Simulation of neutrinos

A dedicated package, called GENHEN (GENerator of High Energy Neutrinos),

has been developed by the ANTARES collaboration to cover the full range of

neutrino (all flavours), from a energy around 10 GeV (approximately the energy

thershold of muon detection) to multi-PeV.

The GENHEN has the following main feature:

• All neutrino interactions are simulated. At high energies the process rele-

vant is Deep Inelastic Scattering (DIS), therefore QuasiElastic (QE) and

Resonances (R) are the dominant processes at low energies;

• Events inside and outside the can are generated together;

• In case of inside events (volume events) hadronic and electromagnetic

showers at the interaction vertex are fully simulated, instead outside events

(surface events) high energy muons and tauons are tracked until they stop

or reach the surface of the can;


• The effect of the different media, manly rocks and water, around the

detector are taking account;

• The generation spectrum of neutrino interactions is expressed with a power

law:

dN

dE= E−Γ (4.1)

This is referred to as interacting neutrino spectrum, which can be weighted

to different neutrino fluxes to produce the event rates.

The simulation strategy is based on the definition of a volume, referred gen-

eration volume, around the detector which contains all potentially detectable

neutrino interactions for the given energy range and simulate it within that vol-

ume.

The users firstly decide the maximum neutrino energy (Emax). This corre-

sponds to an upper limit on the energy of the muon (produced by a νµ via

CC processes) emerging from the interaction vertex, and to a maximum value

of the muon range, Rmax. If the event vertex is contained within the can the

information describing each particle is stored. For neutrino interactions outside

the can, only muons are propagated and the relevant information at the can

surface stored. The scheme of simulation proceeds (fig 4.2) as follow:

• A cylindrical volume (extending the can size) around the detector with a

radius Rmax is defined;

• The total binning neutrino spectrum is divided into equal N bins, between

Emin and Emax. The number of events for each bin is calculated.

• For each energy bin, using the maximum energy in that bin the maximum

muon range in rock and water is estimated.

• For each energy bin the numerical integration of the cross-section in LEPTO

[50] is performed. Therefore the generation for this energy range is ini-

tialised.

• In this step, the loop over the number of events in this scaled volume

(Nscaled) starts:

1. The energy of the interacting neutrino is sampled from the E−Γ spec-

trum within the energy range of this bin;

2. Within the scaled volume, the neutrino position is chosen;

3. If the position is outside the can the shortest distance from the neu-

trino vertex position to the can is estimated. In the case of this

distance is greater than the maximum muon range at that neutrino

energy, the muon don’t reach the can, and the event is rejected;

4.2. GENERATION OF PHYSICS EVENTS 55

Figure 4.2: The scheme of neutrino simulation with GENHEN [14].

4. The neutrino direction is sampled from an isotropic distribution. For

events outside the can, the distance of closest approach of the neu-

trino direction to the can is calculated comparing it to some user

specied distance;

5. For each event, the neutrino interaction is simulated to get the final

state particles produced at the neutrino interaction vertex;

6. At this step, for event inside the can all the properties of these par-

ticles are recorded (position, direction, energy, etc.). Therefore for

events outside only the properties of muons are kept;

7. For those events which are kept, the event weights are calculated

and all the event informations are written on disk.

• On completion of all the stages above, a record of each neutrino interaction

producing at least one particle at or inside the can is obtained.

As mentioned before the events can then be weighted properly to obtain the

effective rates at the detector in according to a specific model. Given a model

with Φ(Eν , θν), the global weight is:

ωglobal = ωgen · Φ(Eν , θν) (4.2)


where ωgen is [14]:

ωgen =Vgen · ρNA · PEarth(E, θ) · Iθ · IE · EΓ · F

Ntotal(4.3)

The terms of equation (4.3) rapresent:

• V[m3] is the Generation volume;

• ρNA is the product between the target density (ρ) and the Avogadro’s

number (NA). This gives the the number of target nucleons per unit

volume;

• σ(Eν) [m2] is the total cross section of neutrino;

• PEarth(Eν ,θν) is the probability that the neutrino cross the Earth;

• Iθ [sr] = 2π(cosθmax − cosθmin) is the angular phase factor depending on

the specied range of cosθν ;

• IE is the enegy phase space factor depending on the input spectral index

(Γ). For Γ = 1, IE = ln(Emax/Emin) otherwise IE = (E1−Γmax−E1−Γ

min )/(1−Γ);

• F is the second in one year;

• Ntotal is the total number of the generated events.

4.3 Light emission and propagation

A specific package KM3 (a GEANT-based software developed in the ANTARES

context) has been developed for a full and quick simulation of the response of the

ANTARES detector. In particular is simulated the production and propagation

of Cherenkov light inducted by all long-lived particles which are stored as output

from the physics generators. The sea water doesn’t present inhomogeneities [14],

consequently it isn’t necessary a photon-by-photon simulation for the Cherenkov

light.

Thus the simulation is obtained with the creation of ’photon tables’ that store

the numbers and the arrival times of the photons. Moreover the tables contains

the probability that each photons give a hit on a PMT. This probability depends

by 5 parameters:

• The distance of the PMT from the particle;

• The 3 angles defining the direction of the photons with respect to the

particle and to the PMT;

4.3. LIGHT EMISSION AND PROPAGATION 57

Figure 4.3: Graphical representation of the concentrical shells used for building

the hit probability tables [14].

• The photon arrival time on the PMT.

The first step for the generation of the photon tables is the simulation of the

light emitted along the path of muon, at step of 1 m. The photon tracking

is performed considering the composition and the density of the water at the

experimental site and its optical properties: absorption and scattering lengths.

The result is a set of tables recording the photon properties: position, direction,

wavelength and time when they cross spherical shells of increasing radii centered

around the track segment, see fig.4.3.

The probability for each Cherenkov photon to reach a PMT is extracted from

these ’photon tables’. Similar tables are calculated for electromagnetic showers.

The hadronic showers are treated differently, due manly for the large number

of charged particles produced at the interaction vertex and the high stochas-

tic variability in the composition of the shower. Therefore the computation

of scattering tables for each single particle would require an event-by-event

simulation and a huge amount of CPU time. A different approch is used:

the ’multi particle approximation’. Each particle of shower is treated as

a electron. The electron ’photon tables’ are used in association with oppor-


tune weights, evaluated for each hadron after many complete photon tracking

simulations.

4.4 Simulation of data acquisition

The software in ANTARES used for the simulation of detector response is Trig-

gerEfficiency. This program is performed to adding the hits of physical event

with the hits of optical background, for simulation of the electronics and trig-

gering of events. There are two strategies to add the optical background:

• The optical background can be generated in according to a Poissonian

distribution with a fixed rate chosen by the users;

• The amount of background light can be extracted from a real data run.

In the first approch the background of 40K (manly) is reproduced, because the

rate is essentially constant. The second approach allows for a more realistic sim-

ulation of the environment situation, because is taken into account for seasonal

variations occurring as a consequence of biological activities and for inefficiency

of the OMs, due to the ageing of the PMTs and to the biofouling on the OM’s

surfaces.

The program simulates the DAQ system as described in §2.3.3. Starting from the

number of photons impinging on the OM an analogue pulse at the PMT anode

is simulated whose charge follows a Gaussian distribution. In order to simulate

the time resolution (for single photo-electron signals is 1.3 ns and decreases for

higher amplitudes), the hit times are smeared using a Gaussian function, with

a width [14]:

σ = 1.3ns/√Nγ (4.4)

where Nγ is the number of simultaneously detected photons.

4.5 Run-by-run approach

In order to reproduce the current detector status at the time of data taking a

Monte Carlo strategy, the so called run-by-run, is used in ANTARES. For each

physics run an analogous MC run is produced using the informations extracted

directly from the data. This approch is achieved to take into account the vari-

ations of the environmental conditions under the sea and the periodical change

of the rates registered at the detector due to the biological and physical phe-

nomena. In addition, not all detector elements take data continuously, due of

temporary or permanent malfunctioning of optical modules or lack of connection

to some part of the apparatus. At the moment, the run-by-run version 4 (rbr

4.5. RUN-BY-RUN APPROACH 59

v4) is produced. Compared to the old version (rbr v3), v4 has new important

features:

• The water model of Capo Passero is used;

• 85% OM collection efficiency;

• Efficiency reduction tables according to the measured 40K coincidence

rates.

Comparing the time evolution of the measured atmospheric muon rate in the

data sample of ANTARES to the simulated rate, a discrepancy between the two

time series has emerged. Same result is noted for neutrino induced events. The

loss of efficiency of the OMs has been analyzed using the measurement of 40K.

This signal is constant and stable (see section 2.4.2), therefore can be used as a

reference.

This study has been performed in [15]. In order to monitor the status of the

whole detector from 2008 to 2017, the average photon detection efficiency as a

function of time has been determined. An average decrease of the OM efficiency

by 20% is observed, as shown in fig 4.4. The results of this study implicate

a realistic simulation of the OM efficiencies in each data taking run. For the

rbr v4 this feature is computed for both muons and neutrinos. For neutrinos

simulation this efficiency shows a good data/MC agreement, instead for muons

this correction cannot reproduce a time dependent behaviour in the data/MC

[52]. The rate of muons event was calculated as:

Rate[Hz] =NselectedEventsRunDuration[s]

(4.5)

where NselectedEvents are the number of events (of muons) that are passed the

following selection (AAfit variables):

• Downgoing events (Zenith < 90◦);

• Λ > −6.5;

• β < 1◦, where β indicates the direction error =√σ2θ + σ2

φsin2(θ) ;

• Trigger 3N.

As shown in fig.4.5, the data/MC is time dependent. This means that the

simulation for muons isn’t able to reproduce the behaviour of data. To correct

the MC behaviour according to the observed data rates, a specific procedure

was used:

• To find the rate loss as function of time, a fit of data/MC ratio is per-

formed;


Figure 4.4: Relative OM efficiency averaged over the whole detector from 2009

to 2017. The blue arrows indicate the periods in which high voltage tuning of

the PMTs has been performed [15].

• Find the correlation between a given efficiency reduction in the MC sim-

ulation and the rate loss;

Combining them the efficiency correction as function of time was obtained [52]:

EfficiencyCorrection =−0.00013 · (dateMJD) + 8.077− 0.99

−1.598(4.6)

This correction has to be applied in addition to the correction that comes from

the 40K study, to remove the time dependence of the rate loss of atmospheric

muon events.

4.5. RUN-BY-RUN APPROACH 61

Figure 4.5: Data/MC ratio, one point per run, as a function of time. Three

regions are indicated: first platau, linear decreasing and second plateau [52].

Chapter 5

Monte Carlo v3 and v4

One of the goal of this thesis is the preliminary study of selection criteria that

can allow a separation between interactions of atmospheric neutrinos producing

a showers and the background of atmospheric muons. As discussed in Chap-

ter 2, showering events are induced by charged current interactions of electron

neutrinos and neutral current interaction of all neutrino flavours. Atmospheric

electron neutrinos are characterized at high energies (above 1 TeV) by a flux

that is more than one order of magnitude smaller than that of muon neutrinos,

with a soft spectral index, Γ ∼ 3.7. The neutrino interactions we are searching

for must be well contained within the detector fiducial volume; the huge flux

of atmospheric muons represents a background that can be almost irreducible.

This work try to estimate how the atmospheric muon flux can be reduced using

different selection criteria. A selection procedure optimized for cosmic electron

neutrinos (characterized by a harder spectral index, Γ ∼ 2, and a flux higher

than that of atmospheric neutrinos at energies higher than 50 TeV, was defined

in [53]. This selection was optimized with the Monte Carlo simulation of the

signal and of the background defined as “rbr v3” in the previous chapter. In this

chapter, it is presented the effect of the same chain of cuts but applied on the

new version of Monte Carlo: the rbr v4. That version includes the correction

for the muon efficiency described in §4.5. The comparison of the effect of the

cuts on the two Monte Carlo versions are presented.

5.1 Event selection

In order to reduce the background given by atmospheric muons, a chain of cuts

has been defined. The description of this selection cut is presented:

63

64 CHAPTER 5. MONTE CARLO V3 AND V4

• Trigger selection: only the events that pass 3N or T3 trigger are selected

T3 or 3N (5.1)

• Contaiment selection: Only events inside a cylindrical volume around

the detector center with a radius ρ and height z are selected

ρtantra < 300 and |ztantra| < 250 (5.2)

The radius and height considered are those reconstructed with the Tantra

algorithm.

• Mestimator selection: A definition of the variable Mestimator was re-

ported in §3.1.2. When a atmospheric muon is reconstructed with a shower

algorithm often a shower vertex that lie far away from the detector bound-

ary and have a large value for MEst. For this reason a cut is implemeted

as follow:

Mestimator < 1000 (5.3)

• Track-Veto: Event track-like are excluded by the follow selection:

Λ > −5.2 β < 1.0 cos(θ) > −0.1 (5.4)

where the variables are reconstructed by the AAfit algorithm.

• Up-going selection: Only the event up-going are selected:

cos(θzenith,tantra) > −0.1 (5.5)

where θ is the Zenith angle reconstructed by the Tantra algorithm.

• Angular error selection: this step requires that the angular error (for

Tantra algorithm) is:

βtantra < 10◦ (5.6)

• GridFit ratio: the GridFit Ratio variable is defined in 3.3. If it is

combined with the number of selected shower hits, this variable gives a

estimable suppression of atmospheric muon:(R

1.3

)3

+

(Nshower

150

)3

> 1 (5.7)

• MuonVeto selection: a likelihood function has been developed to im-

prove the discrimination between showers and atmospheric muons [53] [31].

This likelihood takes into consideration only the hits that coincide with

another hit on the same storey within 20 ns. The PDFs of this likelihood

depends by the following 3 parameters:

5.2. EVENT SELECTION RBR V3 65

– Time residual tres (see §3.1.2);

– N, number of hits that are in −20 < tres/ns < 60;

– The distance d of the hits to the reconstructed shower position;

The likelihood can be written as [53]:

L =∑hits

[log (Pshower/Pmuon) + Pshower − Pmuon] (5.8)

with Pshower = P (N, d, tres|shower) and Pmuon = P (N, d, tres|muon) are

the PDFs built with the Monte Carlo. This likelihood parameter can be

combined with the zenith angle. On events that have been reconstructed

as down-going, a harder likelihood-ratio cut can be applied:

L >

{400, cos(θAAfit) < −0.2

20, otherwise(5.9)

• Charge Ratio: when a muon is recostructed by an algorithm optimized

for shower, it is supposed that the hits induced by the muons arrive earlier

than predicted by a shower hypothesis. Qearly is defined as:

qi in −1000 ≤ tres/ns ≤ −40 (5.10)

and Qon−time:

qi in −30 ≤ tres/ns ≤ 1000 (5.11)

Therefore the charge ratio between Qearly and Qon−time has been studied,

and a selection to improve the shower selection:

log(Qearly/Qon−time) < −1.3 (5.12)

5.2 Event selection rbr v3

In order to test the performances of the algorithm to discriminate (TANTRA)

the shower events from the atmospheric muons, a selection of events with the

criteria exposed previously was performed with the version rbr v3 of Monte

Carlo ANTARES. The selection of events is published in [53], the results are

reported in the following table:


Criterion Condition εatm.µ εatm.ν→any

Triggered 3N or T3 100 % 100 %

Contaiment ρshower < 300m, |zshower| < 250m 53 % 81 %

M-estimator MEst < 1000 40% 66%

Track Veto not selected as muon candidate 40 % 59 %

Up-going cos(θshower) > −0.1 18 % 44 %

Error Estimator βshower < 10◦ 0.66 % 5.0 %

GridFit Ratio(R1.3

)3+(Nshower

150

)3> 1 0.057 % 4.2%

MuonVeto L > 20 or L > 400 if cos(θAAfit) < −0.2 2.9 · 10−4 % 0.41 %

ChargeRatio log(Qearly/Qon−time) < −1.3 1.1 · 10−5 % 0.31 %

Events in 301 days 18.8 163

In the table the first two columns rapresent the name of the criterion and the

applied condition. The effect on the atmospheric muon is presented in column

3 (εatmµ ), while for atmospheric neutrino is shown in column 4 (εatmoν ). The

efficiencies are the ratio of the number of events that passed a cut and the

number of events after the trigger selection:

εi =npassedntriggered

i = µ, ν (5.13)

As shown on the table above, after applying these selection criteria to the

ANTARES data with an effective life time of 1690 days, is expected to ob-

tain 163 of atmospheric neutrinos. Therefore, the events of atmospheric muon

has been reduced by six orders of magnitudes.

5.3 Event selection rbr v4

The main purpose of this thesis is to calculate the efficiency (eq. 5.13) of the

selection chain for the new version of Monte Carlo ANTARES (rbr v4), in order

to compare with the rbr v3. Therefore the following step was performed:

• A sample of Monte Carlo files are chosen. Only the runs that contained

all types of particles (even all types of processes) were taken into account

(νe CC, νe NC, νµ CC, νµ NC, µ);

• The run ending with 0 are selected. This allows to test a 10% subsample

of the data set leaving blinded the remaining 90%.;

• The lifetime of MC files is 2774 days, instead the lifetime for data is 310

days. Therefore the MC is scaled to the data;

• The selection chain described in the section §5.1 is applied;


Figure 5.1: Likelihood Ratio (Muon-Veto) distribution for atmospheric neutri-

nos (red), atmospheric muons (gray), showers caused by astrophysical neutrinos

(orange), and data (black) [53]. The distribution is plotted after the Gridfit Ra-

tio cut and all previous cuts listed in § 5.1.

Figure 5.2: The Likelihood Ratio distribution after the MuonVeto (rbr v3)[53].

• After each cut a plot of the Likelihood Ratio is produced.

In this section the plots of the Likelihood Ratio distribution are shown. These


plots have been realized to every step of the selection chain in order to observe

the change of the distribution. All plots are shown with the 0-runs data (black

line), the atmospheric muons (grey line) and the atmospheric neutrinos (red

line). Analyzing the plots obtained with v4

(a) Likelihood distribution without cut (b) Likelihood distribution after trigger selec-

tion

(c) Likelihood distribution after contaiment

cut

(d) Likelihood distribution after Mestimator

cut

Figure 5.3: Starting from the plot on the top left side are applied all cuts of the

cut-chain that are the trigger, the contaiment and the Mestimator.The selection

step is shown below each plot. Furthermore, for each plot is showed the ratio

between the data (0-runs) and the Monte Carlo.


(a) Likelihood distribution after Track-Veto (b) Likelihood distribution after up going se-

lection

(c) Likelihood distribution after angular error

selection

(d) Likelihood distribution after GridFit ra-

tio

Figure 5.4: Starting from the plot on the top-left side are applied cuts of the

cut-chain that are the Track-Veto, the up-going selection, the angular error

selection and the GridFit-Ratio. Furthermore, for each plot is showed the ratio

between the data (0-runs) and the Monte Carlo.


(a)

Figure 5.5: The Likelihood distribution after the MuonVeto cut (rbr v4).

5.4. RESULTS 71

5.4 Results

The efficiencies obtained for the v4 are shown in the following tables. The first

table shows the efficiencies for atmospheric muons and neutrinos (either yielding

a shower or a track), in order to then compare with the table in § 5.2 :

Criterion Condition εatm.µ εatm.ν→any



M-estimator MEst < 1000 51% 29%

Track Veto not selected as muon candidate 51 % 28 %

Up-going cos(θshower) > −0.1 34 % 10%

Error Estimator βshower < 10◦ 10 % 4.1 %

GridFit Ratio(R1.3

)3+(Nshower

150

)3> 1 0.016 % 1.7%

MuonVeto L > 20 or L > 400 if cos(θAAfit) < −0.2 1.0 · 10−2 % 0.09%

ChargeRatio log(Qearly/Qon−time) < −1.3 9.3 · 10−3 % 0.07 %

While in the second table is reported the efficiency for atmospheric νe and νµ(CC and NC process):

Criterion Condition εatmoνe εatm.νµ



M-estimator MEst < 1000 20% 29 %

Track Veto not selected as muon candidate 20% 29%

Up-going cos(θshower) > −0.1 8% 10%

Error Estimator βshower < 10◦ 4.6% 4%

GridFit Ratio(R1.3

)3+(Nshower

150

)3> 1 1.9% 1.7%

MuonVeto L > 20 or L > 400 if cos(θAAfit) < −0.2 0.1% 0.08%

ChargeRatio log(Qearly/Qon−time) < −1.3 0.1% 0.08%

The efficiency of the cuts between the two simulations is different. In particular,

in two precise cuts are observed a relevant difference:

• Contaiment: with this selection, a significant reduction in the number

of atmospheric neutrinos is observed for version v4. In version v3 80% of

neutrinos are observed, while with v4 only 32% of events remains. For

muons at this step, instead, there is almost an agreement between the two

versions.

• MuonVeto (and ChargeRatio): these cuts with the rbr v4 version

are less effective than with the rbr v3 version. Therefore with the v3


was obtained a decrease of 10−5%, with v4 only a reduction of 10−3% is

obtained.

Therefore using the cut-chain proposed in [53] with the version rbr v4 is ob-

served a lower efficiency to reject the atmospheric muon events than the rbr v3.

The events of muons decrease for each cut, but a considerable reduction isn’t

achieved. Furthermore, as shown in fig.5.5, the number of neutrinos after the

cut-chain remains low with respect to muon events.

Conclusions

The ANTARES telescope was completed in 2008 and has taken data contin-

uously since then. In order to understand the detector’s response, a chian of

simulation (Monte Carlo) is performed. The main purpose of this thesis is to

make a comparison between two different Monte Carlo simulation of ANTARES:

the old version (rbr v3) and new version (rbr v4).

To accomplish this objective, a cut-chain optimized to reject atmospheric muons

was used as a comparison test. The variables used in this selection of events are

explained in this thesis. Applying these cuts we observe a difference between the

two versions of simulation: the efficiency to reject muon events is less in the new

version (v4) than in v3. Therefore with the version v4 the atmospheric muons

events remain higher than the atmospheric neutrino events, after the cut-chain.

In the end we are focused on the Likelihood Ratio: a function developed to

improve the discrimination between showers and atmospheric muons.

With the version v3, selecting with this Likelihood distribution it was possi-

ble to reduce of six orders of magnitude the background given by atmospheric

muons.

Instead for the v4 version selecting with this distribution is observed a reduction

in efficiency to reject atmospheric muons.

73

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