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University of Amsterdam

Bachelor Thesis 15 EC

Conducted between 30-03-2020 and 17-07-2020

Performance studies of

KM3NeT-ORCA for dark matter

detection with low energy neutrinos

Author:

Bjarne Bouwer

11280454

Supervisor:

Dr. Suzan du Pree

Second assessor:

Dr. Shin’ichiro Ando

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Abstract

KM3NeT is a water-based Cherenkov neutrino detector currently in develop-ment. For this study we focus on ORCA, optimized for measuring low-energy neutrinos. Using reconstructed neutrino events from simulations, we have ob-tained the energy resolution and effective acceptance, the efficiency of a neutrino detector to detect neutrino events, for all neutrino flavors for charged current in-teraction. The events used for this study are selected from the simulated dataset using certain quality cuts. Further study of this method and results can be help-ful for detecting neutrinos originating from dark matter decay or annihilation by estimating signal and background events with the effective acceptance.

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Contents

1 Introduction 3

2 Background Information 4

2.1 KM3NeT neutrino telescope . . . 4

2.2 Neutrino interactions . . . 5 2.3 Neutrino detection . . . 6 3 Methodology 7 3.1 Simulations . . . 7 3.2 Reconstruction . . . 7 3.3 Effective area . . . 8 3.4 Analysis . . . 9 4 Results 11 5 Discussion 17 6 Conclusion 18 7 Acknowledgements 18

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1

Introduction

The neutrino is a strange and fascinating particle. Since its decovery it has answered many questions [1], but raised even more [2]. With the ability to go through the Earth, it is a difficult task to build a detector which can actually detect these particles. The solution is to go big. With predecessors such as the ANTARES experiment [3], it was possible to set up an even larger experiment, namely the KM3NeT experiment. A larger detector volume increases the likelihood that a neutrino interacts within a measurable range.

Neutrino detection is of particular interest for dark matter search. Dark matter only interacts via gravitation as far as we know. However, dark matter might possibly decay and annihilate with itself. Neutrinos are possibly released during this process which can be detected. For dark matter decay the expected number of events per energy and angular bin is Ni,j = Z i dEobs Z j dΩ dI dEν dEν dEobs AeffT h e−τ (Eν)i,

where i and j denotes the energy and angular bin, respectively, dI/dEν the neutrino

intensity, Eobs the observed energy in the detector, Aeff the effective area, T the

expo-sure time and τ (Eν) the absorption factor for Earth [4]. One of the values looked at

in this study, Aeff, appears in this formula and will be explained in detail in section

3.3. Knowing this value precisely for the given neutrino detector will help to estimate signal and background events and improve the search for dark matter.

KM3NeT-ORCA is at the time of writing this thesis in an early phase of deployment and obtaining data. Instead, simulations of the full detector are used. This study gives preliminary results and a picture of what can be expected from the full detector.

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2

Background Information

2.1

KM3NeT neutrino telescope

KM3NeT is a collection of new generation neutrino detection units designed to answer the topical questions about neutrinos. These units are situated in the Mediterranean Sea. In total there are three detectors, so-called building blocks. Two of the building

blocks will be used for detecting high-energy neutrinos (102 to 108 GeV). These two

blocks, located near Sicily, are referred to as ARCA: Astroparticle Research with Cos-mics in the Abyss. The single block will be used for low-energy neutrino detection (up to 100 GeV). This block is referred to as ORCA: Oscillation Research with Cosmics in the Abyss. ORCA is located south of France.

Data from ARCA will be used for observation of high-energy neutrino sources which is a key factor in high mass DM searches. ARCA will not be discussed in detail in this research.

ORCA provides data which will be used primarily for study of neutrino oscillations. As neutrinos have a very small but non-zero mass and the flavor and mass eigenstates are not the same, they can oscillate between the different flavors. This behavior is seen best in the low energy range. ORCA, and especially the performance and efficiency of the detector, will be the main subject of this research.

Each building block will consist of 115 strings, or lines, each with 18 digital optical modules (DOM) attached. A DOM is a glass sphere which houses 31 photo-multiplier tubes (PMT). A PMT can detect the Cherenkov light emitted by charged particles, as will be explained in section 2.3 [5].

Figure 1: A photo of a digital optical module. The photo-multiplier tubes are inside the holes. Figure taken from the Letter of Intent for KM3NeT [5].

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2.2

Neutrino interactions

In nature there are four fundamental forces: the electromagnetic, strong, weak and gravitational forces. Neutrinos can only interact through the weak and gravitational force. For neutrino detectors, such as KM3NeT, only the weak interaction is rele-vant.

The weak interaction can be categorized in two types: neutral current (NC) interactions and charged current (CC) interactions. The NC process happens through the exchange

of a neutral Z boson. For CC processes the force carrier is a charged W± boson

[6].

Figure 2: Feynman diagrams of examples for a charged current interaction (left) and a neutral current interaction (right). Figures are taken from [7].

CC interactions involving electron (anti)-neutrinos and a nucleon result in a particle shower. The relevant interaction is

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νe + N → e±+ h, (1)

where N is the interacting nucleon and h is the hadron in the final state. The electron evolves into an electromagnetic shower by emitting photons through bremsstrahlung

which in turn produce e+e−-pairs. The hadron produces a hadronic shower which

is more complex than the electromagnetic shower due to varying particles and decay modes. Tau (anti-)neutrinos create similar outcomes due to decay because of the short lifetime of the τ lepton [8]. The τ lepton quickly decays inside the detector and creates

showers. The νe and ντ interactions are categorised as a shower event.

For muon (anti)-neutrinos, the CC interactions generally do not lead to electromagnetic showers. A muon does not lose as much energy as an electron by bremsstrahlung due to the muon’s larger mass, and its decay time is not short enough to decay often inside the detector. As a result, muons tend to travel in a straight line through the detector. This signature is called a track event.

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2.3

Neutrino detection

Neutrinos can not be detected directly, but instead through their effects on other particles. When a neutrino interacts with a charged particle in the water, the charged particle will gain a large amount of energy. If a charged particle goes faster than the speed of light in that medium the particle emits Cherenkov radiation, analogous to a sonic boom. The radiation is emitted in a cone as can be seen in figure 3. The opening angle θ depends on the refractive index n of the medium and the velocity β of the charged particle:

cos θ = 1

βn. (2)

Assume the particle is highly relativistic so β = v

c ≈ 1, and use n ≈ 1.35 for seawater,

then the opening angle θ becomes 42◦ [9].

The light from the Cherenkov radiation can be detected by the PMTs on the lines. Combining the signals can be used to reconstruct the track taken by the muon, which also gives information about the path of the neutrino because of the kinematics involved in the interaction. More details on the reconstruction process will be given in section 3.2.

Figure 3: An illustration of the Cherenkov cone effect. The angle θ depends on the velocity u of the charged particle. Figure taken from [10].

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3

Methodology

3.1

Simulations

Simulations can be used to prepare and test analysis software which will eventually be used for the actual data. It gives an idea of what can be expected of the detector and what is possible. The KM3NeT collaboration uses the Monte Carlo method to simulate the interactions, propagation of the particles and the detector response. Part of the simulation software has been created specifically for KM3NeT while the other part has been adapted from the ANTARES collaboration, the predecessor of KM3NeT [5].

Simulating each and every neutrino arriving at Earth would be very time-consuming and inefficient to do, as Earth receives a very large amount of neutrinos every second. Neutrinos have a very small interaction cross sections so instead a different approach has been chosen: every simulated neutrino is certain to interact. This significantly reduces the computing time for each simulation. To account for this choice, the events need to be weighted to the actual neutrino flux. The weight used for this research is described in detail in the paper about the neutrino simulation software, gSeaGen [11].

3.2

Reconstruction

Reconstruction is combining the measured or simulated DOM responses to recreate the neutrino event. As there are track and shower events that can occur when a neutrino interacts, different reconstruction algorithms are needed for the different types of events. For this study, the internal software packages JGandalf and Dusj were used for track and shower events, respectively. For both algorithms, the best reconstruction is chosen as the one with the highest likelihood.

For track reconstruction the important parameters are the energy and the track length of the muon. The track length and direction is estimated by combining the informa-tion from the PMTs and the Cherenkov radiainforma-tion arrival times. Since the Cherenkov radiation travels like a cone, it is possible to trace back the taken path of the muon, see figure 3. In order to obtain the best possible reconstructed event, there is a chain of steps for the reconstruction process. First there is a pre-fit of the track. In the next step the full fit is done by using the pre-fit as a base and choosing the fit with the maximum likelihood. The starting position is then found by projecting the PMT signals back on the track. At last the muon energy is estimated based on the track length as found in the previous steps. The energy and direction of the neutrino can then be obtained by combining the track length and the kinematics involved in the interaction [12][5].

Reconstructing a shower event is similar to the process for a track event. As a shower event contains many particles emitting Cherenkov radiation, it is harder to find the

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direction of the shower. However the main particle originating from the neutrino in-teraction gives off the brightest Cherenkov cone so the shower direction can be recon-structed. First off the interaction position (vertex) is reconrecon-structed. This is actually done by finding the brightest point of the shower, which is not the actual vertex, and then applying an offset to obtain the vertex. The energy and shower direction then can be found from the angular light distribution.

3.3

Effective area

Effective area is a tool to quantify the efficiency of a neutrino detector to detect neutrino events. It relates the rate of detected particles R and the incident neutrino flux Φ in a given solid angle Ω and time:

R(Eν, Ω) = Aeff(Eν, Ω) × Φ(Eν, Ω), (3)

where Aeff is given in [m2]. The quantity is unique to the detector and the interaction,

e.g. the effective areas for νe and νµ interactions are different [5]. If a binned energy

and angle range is used in analysis, the effective area can be given by

Abineff = N

TRbinΦ dE dΩ,

where N is the total number of events. T is the time in seconds in one year because N is given in number of events per year and the denominator is number of neutrinos per second. The neutrino flux Φ is assumed to be isotropic, simulated as a power law

Φ = E−γ, where γ is the spectral index. This index is a parameter chosen differently

in the simulations for each neutrino flavour and energy range. Using the power law for the flux, the integral evaluates to

Z bin Φ dE dΩ = Z bin E−γdE dΩ = Z Ω dΩ Z Ebin E−γdE = Ω E 1−γ 1 − γ Emax Emin = Ω E 1−γ max− E 1−γ min 1 − γ

where Ω is the solid angle of the bin and Emin and Emax are the energy ranges of the

bin.

N can be given in terms of the number of generated events Ngen from the simulations,

the weight w from section 3.1 and the neutrino flux Φ summed over every event i for a given bin:

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N = 1

Ngen

X

i

wiΦ.

Finally, the effective area can be given as

Abineff = 1 − γ Ω T Ngen P iwiΦ Emax1−γ − Emin1−γ . (4)

3.4

Analysis

Data analysis has been performed with the use of ROOT and Python. ROOT is a data analysis program developed by CERN and can be integrated in Python with PyROOT, a Python module [13].

Reconstructing events is not without errors and certain events can be badly recon-structed. Eliminating these bad events increases the quality of the dataset. We can do that by cutting on quality parameters to remove outliers. In all cases only upgoing events are selected, from here on referred to as ”trigger upgoing”. This means the par-ticles have to travel through Earth to reach the detector. Neutrinos easily do so, but other particles generally do not. For all flavors, the quality cuts are based on a score given by a machine learning algorithm called a PID score, which is the particle identi-fication score. All simulated events are recontructed as a track and as a shower event. The algorithm then gives a score to the reconstruction to indicate how much it looks like a track or a shower event. If a reconstructed event has a bad score corresponding

to its reconstruction type it is removed from analysis. For νe and ντ this selection is

referred to as ”quality cuts”. For the νµ CC analysis we cut on the likelihood of the

reconstructed event and the angular reconstruction. The angle between the simulated

track and the reconstructed angle can not be too large, so a maximum difference of 8◦

has been chosen. We refer to this selection as ”loose cuts”. The algorithm cut can be applied on top of this selection, referred to as ”tight” cuts.

The goal of this project is to study the efficiency of the ORCA detector. As discussed before, this can be illustrated with the effective acceptance. In addition the energy resolution of the detector is looked at. The energy resolution for a small windows of energies is obtained by fitting a Gaussian function to the distribution of reconstructed energies around a given simulated energy, that is

∆E

Esim

= Ereco− Esim

Esim

. (5)

The standard deviation of the Gaussian function is then taken as the energy resolution of the given energy, see figure 4. Repeating this analysis for several energies gives the energy resolution for a wide range.

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h_energyreso Entries 3580 Mean 0.04051 Std Dev 0.2404 / ndf 2 χ 41.63 / 16 Constant 598.6 ± 13.1 Mean 0.04221 ± 0.00398 Sigma 0.2358 ± 0.0033 1 − −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 sim E / E ∆ 0 100 200 300 400 500 600 h_energyreso Entries 3580 Mean 0.04051 Std Dev 0.2404 / ndf 2 χ 41.63 / 16 Constant 598.6 ± 13.1 Mean 0.04221 ± 0.00398 Sigma 0.2358 ± 0.0033

Reconstructed energy distribution around 11 GeV

(a) h_energyreso Entries 386 Mean −0.04599 Std Dev 0.1719 / ndf 2 χ 11.03 / 9 Constant 111.7 ± 7.5 Mean −0.03003 ± 0.00701 Sigma 0.134 ± 0.006 1 − −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 sim E / E ∆ 0 20 40 60 80 100 h_energyreso Entries 386 Mean −0.04599 Std Dev 0.1719 / ndf 2 χ 11.03 / 9 Constant 111.7 ± 7.5 Mean −0.03003 ± 0.00701 Sigma 0.134 ± 0.006 Reconstructed energy distribution around 50 GeV

(b) h_energyreso Entries 273 Mean −0.04233 Std Dev 0.1437 / ndf 2 χ 4.685 / 7 Constant 97.21 ± 7.28 Mean −0.03121 ± 0.00680 Sigma 0.1103 ± 0.0049 1 − −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 sim E / E ∆ 0 20 40 60 80 100 h_energyreso Entries 273 Mean −0.04233 Std Dev 0.1437 / ndf 2 χ 4.685 / 7 Constant 97.21 ± 7.28 Mean −0.03121 ± 0.00680 Sigma 0.1103 ± 0.0049 Reconstructed energy distribution around 80 GeV

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Figure 4: Distribution of reconstructed energies as defined in equation 5 around 11, 50

and 80 GeV simulated neutrino energies from the νe analysis. For higher energies the

spread of reconstructed energies becomes thinner. The sigma of the fitted Gaussian function is the energy resolution of the given energy.

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4

Results

All results are obtained for νe, νµ and ντ CC interactions from 10 to 100 GeV unless

stated otherwise and are averaged for ν and ¯ν. The simulations are run for the complete

detector with 115 strings.

The effectiveness of the quality cuts is shown in figure 5. In the figure the survival fraction of the events is displayed for the upgoing cuts and for additional quality cuts. Trigger upgoing has a survival rate of 0.5, as the upgoing events are half of the total events from all directions. The quality selection cut more events in the lower energy

range for νµ and the opposite is true for νe and ντ. This difference could come from

the fact that events involving νµ are reconstructed as track events whereas the other

flavors are reconstructed as shower events.

10 20 30 40 50 60 70 80 90 100 Esim [GeV] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Fraction e CC Trigger upgoing Quality cuts (a) 10 20 30 40 50 60 70 80 90 100 Esim [GeV] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Fraction CC Trigger upgoing Loose cuts Tight cuts (b) 10 20 30 40 50 60 70 80 90 100 Esim [GeV] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Fraction CC Trigger upgoing Quality cuts (c)

Figure 5: The cut efficiency for all neutrino flavors. On trigger level half of all events is remaining as the upgoing cut effectively cuts away half of all events. The event selection appears to have a different signature for track and shower events. The selection removes more shower events for higher energies and the opposite for track events.

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The energy resolution, determined as described in section 3.4, is plotted in figure 6

for all three flavors for the range from 1 to 100 GeV. For νe and νµ, data from the

Letter of Intent for KM3NeT has been plotted in the same figure to compare. For

ντ this data does not exist in the Letter of Intent. In general the energy resolution

improves when quality cuts are applied. As the energy increases the resolution also

gets better. For νe and νµ the resolution is particularly bad for very low energies. At

these energies based on the amount of produced light and travel length of the involved particles good reconstruction might be difficult. As the energy increases reconstruction is easier as the signals are more distinguishable. Currently there are ongoing studies in the KM3NeT collaboration targeting the improvement of the reconstructions for low energy particles. 0 20 40 60 80 100 Esim [GeV] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 /Esim e CC Trigger upgoing Quality cuts LOI 9m (a) 0 20 40 60 80 100 Esim [GeV] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 /Esim CC Trigger upgoing Tight cuts LOI 9m (b) 0 20 40 60 80 100 Esim [GeV] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 /Esim CC Trigger upgoing Quality cuts (c)

Figure 6: Energy resolution for trigger upgoing and quality cuts. Data from the Letter

of Intent for KM3NeT is shown in red to compare for νe and νµ. The 9m indicates a

vertical spacing of the DOMs of 9 meter, which is being used for both the simulations and the current deployment of the detector.

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The reconstructed zenith angle dependence for the effective acceptance can be seen in figure 7. It is only shown for upgoing events, as the downgoing events are removed. The effective area stays mostly flat except for the almost horizontal events. For both

νe and νµ the effective area starts to go down. This might happen because the event

can be reconstructed as downgoing while it is simulated as upgoing due to the leeway in angular reconstruction. 1.0 0.8 0.6 0.4 0.2 0.0 cos(zen) 0.0 0.2 0.4 0.6 0.8 1.0 Ef fe cti ve ar ea [m 2] ×104 e CC Trigger upgoing Quality cuts (a) 1.0 0.8 0.6 0.4 0.2 0.0 cos(zen) 0.0 0.2 0.4 0.6 0.8 1.0 Ef fe cti ve ar ea [m 2] ×104 CC Trigger upgoing Loose cuts Tight cuts (b) 1.0 0.8 0.6 0.4 0.2 0.0 cos(zen) 0.0 0.2 0.4 0.6 0.8 1.0 Ef fe cti ve ar ea [m 2] ×104 CC Trigger upgoing Quality cuts (c)

Figure 7: Effective area for reconstructed zenith angle. There is a restriction on the simulated zenith angle, only allowing upgoing events, cos(zen) < 0. Only events with energies from 10 to 100 GeV have been used.

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10 20 30 40 50 60 70 80 90 100 E [GeV] 105 104 103 Ef fe cti ve ar ea [m 2]

ORCA effective area e CC

Trigger upgoing Quality cuts (a) 10 20 30 40 50 60 70 80 90 100 E [GeV] 105 104 103 Ef fe cti ve ar ea [m 2]

ORCA effective area CC

Trigger upgoing Loose cuts Tight cuts (b) 10 20 30 40 50 60 70 80 90 100 E [GeV] 105 104 103 Ef fe cti ve ar ea [m 2]

ORCA effective area CC

Trigger upgoing Quality cuts

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Figure 8: Effective area of KM3NeT-ORCA with respect to the simulated energy of the three types of neutrinos. The curve is obtained by taking the center and effective area of the energy bins. After event selection there is a reduction in the effective acceptance as there are less events.

The energy dependence for the effective acceptance has been determined for all three flavors, shown in figure 8. The effective acceptance increases as the energy increases for all flavors. Quality selection reduces the acceptance compared to only trigger upgoing.

This reduction is more apparent for νe and ντ than for νµ. Two comparisons between

a similar study done for ORCA and ARCA are shown in figure 9, only for νe and νµ.

For the ORCA comparison, the trends are very similar. The difference in effective area is due to a different horizontal spacing of the lines. This study uses a smaller horizontal spacing, 20 meter instead of 23 meter, effectively reducing the total volume of the detector. The event selection is done with the previously described algorithm cut as well.

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10 20 30 40 50 60 70 80 90 100 E [GeV] 105 104 103 Ef fe cti ve ar ea [m 2] e CC

KM3NeT internal: trigger upgoing KM3NeT internal: quality cuts This work: trigger upgoing This work: quality cuts

(a) 10 20 30 40 50 60 70 80 90 100 E [GeV] 105 104 103 Ef fe cti ve ar ea [m 2] CC

KM3NeT internal: trigger upgoing KM3NeT internal: quality cuts This work: trigger upgoing This work: loose cuts This work: tight cuts

(b) 101 102 103 104 105 106 107 108 E [GeV] 106 104 102 100 102 Ef fe cti ve ar ea [m 2]

ORCA + ARCA effective area ( + )

This work ORCA: CC tight cuts This work ORCA: e CC final

KM3NeT internal ARCA: CC KM3NeT internal ARCA: e CC

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Figure 9: Comparisons of effective areas to other internal studies performed for ORCA

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Finally, the effective acceptance dependence for both the energy and zenith angle is shown in figure 10 for upgoing trigger and with event selection for all three flavors in two-dimensional plots.

(a) νe trigger upgoing (b) νe quality cuts

(c) νµ trigger upgoing (d) νµ tight cuts

(e) ντ trigger upgoing (f) ντ quality cuts

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5

Discussion

From the data we obtained the cut efficiency, energy resolution and effective acceptance of KM3NeT-ORCA for all flavors. Event selection was introduced to filter out badly reconstructed events. Comparisons to other studies are included as well.

The event selection improves the quality of the reconstructed events as expected. This can be seen in the energy resolutions in figure 6. After event selection, the resolution improves for all neutrino flavors since the spread of reconstructed energies becomes closer to the simulated energy. However, the effective acceptance does decrease after quality cuts. This is due to removing events and thus lowering the rate of measured particles. The event selection could be subject for futher study. The goal of the selection is to remove the badly reconstructed events. Stricter selection increases the quality but also leaves you with a smaller amount of events to analyze. To a certain degree this side effect can be negated by using more data.

When comparing the results to the internal KM3NeT study for ORCA, the internal study has a larger effective acceptance. This is mostly due to the larger horizontal

spacing of the lines. Increasing this spacing effectively increases the total volume

of ORCA and therefore the effective acceptance. The 20 meter horizontal spacing has recently been adapted for the simulations and the detector, as it increases the sensitivity for the neutrino mass hierachy. On top of that, different event selection was

used. This is most likely the reason for the slope difference in the νµ graph. We also

compared the obtained effective acceptances to ARCA.

The results obtained are only preliminary, not taking several effects into account. Those

effects include, but are not limited to, random noise from the electronics,40K decay and

bioluminescence. In further study, considering some of these effects gives more realistic results. The NC interaction should be taken in account as well. An incoming neutrino can undergo either a CC or a NC interaction. To find the total effective acceptance for a neutrino flavor, both current interactions need to be considered. The picture is simply not complete with only CC interactions.

As far as dark matter searches, conclusions can be drawn based on figure 9c. Outgoing particles from dark matter decay or annihilation have energies tied to the mass of the dark matter particles. As can be seen from the figure, ARCA simply has a wider en-ergy range and therefore can detect signals from a larger dark matter mass range. In addition, the effective acceptance for ORCA is very small compared to ARCA leading to less signals from the dark matter. Nonetheless, optimizing dark matter search for ORCA is essential for very low dark matter masses as ARCA can not detect such sig-nals. Even thought dark matter search is not the main objective of ORCA, making use of ORCA for dark matter search can be complimentary to the search with ARCA.

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6

Conclusion

In this study the performance of the KM3NeT-ORCA detector was the subject of research. The effective acceptance and energy resolution with respect to energy and zenith angle were determined for all flavors for CC interactions. These results were considered with and without quality selection. The quality selection increases the performance of the detector while not reducing the effective area significantly. The obtained results were compared to an internal study and to ARCA.

For further research, several changes can be made. First, the NC interaction should be studied as well to find the full effective acceptance for a certain neutrino flavor. Second, random noise can be injected in the simulations for a more realistic scenario. Finally, the method used could be studied more in detail with regards to event selection and optimisation towards real data.

7

Acknowledgements

I want to thank the KM3NeT/ANTARES group of Nikhef for helping me when needed and providing guidance, especially Suzan du Pree and Lodewijk Nauta for their excel-lent advice and feedback.

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References

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[3] ANTARES Collaboration. Overview of the ANTARES experiment. url: www .

antares.in2p3.fr/Overview/index.html.

[4] Kenny Ng et al. “Sensitivities of KM3NeT on decaying dark matter”. In: (2020).

arXiv: arXiv:2007.03692.

[5] KM3NeT Collaboration. “Letter of intent for KM3NeT 2.0”. In: Journal of

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ch/record/677618/files/p115.pdf.

[7] J. Hofest¨adt. “Measuring the neutrino mass hierarchy with the future KM3NeT/ORCA

detector”. Friedrich-Alexander University Erlangen-N¨urnberg, Feb. 2017.

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2018), p. 030001. doi: 10.1103/PhysRevD.98.030001.

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University of Amsterdam, June 2004.

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8

Populair wetenschappelijke samenvatting

Ongeveer een eeuw geleden hebben wetenschappers er bijna voor gekozen om ´e´en van

de basisprincipes van de natuurkunde, het behoud van energie, weg te doen. Zij zaten

namelijk te worstelen met verloren energie bij b`etaverval. Bij dit verval wordt een

neutron omgezet naar een proton, of andersom, en komt er nog een elektron of positron

vrij. Althans, dat dachten ze. Als je op deze manier de energie¨en uitrekent van de

vrijgekomen deeltjes komt dat niet overeen met wat je ziet in het laboratorium. Om

het wel eenstemmig te maken, opperde Wolfgang Pauli dat er n´og een deeltje vrijkwam:

een neutrino. Na alle berekeningen opnieuw gedaan te hebben was de theorie gelukkig weer eenstemmig met de werkelijkheid.

Toch heeft het vele jaren geduurd voordat het deeltje definitief ontdekt was. De re-denen hiervoor? Een korte beschrijving van de neutrino: razendsnel, praktisch

massaloos en vrijwel onzichtbaar. Het

vliegt door alles heen en wordt daarom ook wel het spookdeeltje genoemd. De enige reden dat we het deeltje kunnen zien is omdat er zo gigantisch veel van zijn. Als er maar genoeg zijn kom je er vanzelf wel eentje tegen. Sinds zijn

ont-dekking heeft de neutrino veel stof doen opwaaien binnen de natuurkunde en daarom willen we toch heel graag het deeltje meten.

Hoe kan je dat doen als het overal doorheen vliegt? Het antwoord is simpelweg om een stuk zee om te bouwen. Uit een samenwerking tussen meerdere universiteiten en onderzoeksinstituten is dat gebeurd en wordt KM3NeT gebouwd. Om de neutrinos te meten worden een heleboel lange lijnen in de Middellandse Zee gehangen met daaraan lichtsensors. Deze sensors kunnen het licht zien wat vrijkomt als een neutrino botst met het water. Met het gemeten licht wordt geprobeerd om de botsing na te bootsen. Aan de hand daarvan kan de richting en energie van de neutrino worden berekend. In mijn onderzoek heb ik gekeken hoe goed KM3NeT is in het meten en nabootsen

van die deeltjes, dus hoe effici¨ent KM3NeT is. Aan de hand van mijn resultaten kan

de zoektocht naar donkere materie, ook spookdeeltjes, hopelijk verbeterd worden. We denken dat donkere materie ook kan vervallen, net zoals een neutron of proton bij

b`etaverval, en daarbij komen dan neutrinos vrij. Als we een heleboel neutrinos meten

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