Search for a correlation between HiSPARC cosmic-ray data and

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M ^ASTER ’ ^{S THESIS} P ^HYSICS

C

OMMUNICATION AND

E

DUCATION

V

ARIANT

Search for a correlation between HiSPARC cosmic-ray data and

weather measurements

Author:

Loran de Vries Supervisor:

Prof. dr. Ing. Bob van Eijk, University of Twente Second reviewer:

Dr. Els. de Wolf, University of Amsterdam, Institute of Physics

National institute for subatomic physics, Amsterdam

August 7, 2012

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The number of events per hour and the most probable value (MPV) of the PMT pulse heights registered by HiSPARC cosmic-ray detectors fluctuate over time. I have developed linear models that attribute these fluctuations to atmospheric variables. I found a strong negative correlation between the number of events per hour and the atmospheric pressure and a weak negative correlation between the number of events per hour and the outside air temperature. No correlation was found between the number of events per hour and the outside relative humidity or the solar radiation. I have developed a model that describes the fluctuations in the number of events per hour using atmospheric pressure and outside air temperature at sea level. The model resulted in χ²r in the range 1.15- 1.60. I found a medium correlation between the most probable value of the pulse heights for a three hour period and the temperature of the photomultiplier tube (PMT). The PMT temperature can be described with a linear model that incorporates the outside air temperature and the solar radiation at sea level. I have developed a model that describes the MPV fluctuations using outside air temperature and solar radiation. The model resulted in correlation coefficients in the range r = 0.54 to r = 0.64. To acquire weather data I developed LabVIEW software that reads data from a Vantage Pro weather station and sends this to the HiSPARC local database. Moreover, I developed correlation software for high school students using Python. The software requires no prior knowledge of Python itself. High school students can download, plot and analyze HiSPARC cosmic-ray and weather data in the classroom.

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Chapter 1

Introduction

The Earth is constantly bombarded with subatomic particles called cosmic rays. If a cosmic-ray particle (most probably a proton) collides with an air nucleus (probably a nitrogen or oxygen nucleus) they will interact strongly and create new secondary particles (mostly pions). These secondary particles will collide with other air nuclei and create even more particles. In this way, a shower of particles develops in the atmosphere. Most secondaries are not stable particles and will therefore decay. If the energy of the incoming cosmic-ray particle is large enough the shower will reach sea level. More information about cosmic rays is found in chapter 2.

At sea level the HiSPARC detectors measure the secondaries and their decay products using scintillation detectors. These detectors are positioned at the rooftops of high schools and universities. They are build by high school students using the resources of HiSPARC (High School Project on Astrophysics Research with Cosmics). The detectors are described in chapter 3.

Figure 1.1: HiSPARC logo [1]

The high school students get the opportunity to be a part of real scientific research. High school students build cosmic-ray detectors under supervision of university staff. Once the detectors are installed on the roofs of high schools or universities, the high school students can analyze shower data for their research projects.

HiSPARC focuses on the detection of Ultra High Energy Cosmic Rays (UHECRs).

If a shower produced by a UHECR reaches ground level the surface area of the shower can be around 1 km². This is approximately the average distance between high schools in a large city. If we determine the density of secondary particles in a shower at several points we can determine the properties of the incoming cosmic ray using computer simulations. This is the reason that detection stations are grouped in clusters at relatively short distances. Currently we have seven clusters:

1. Amsterdam (NIKHEF)

2. Utrecht (Utrecht university and Utrecht university college) 3. Nijmegen (Radboud university)

4. Leiden (Leiden university)

5. Groningen (Groningen university)

6. Enschede (University of Technology Twente) 7. Eindhoven (University of Technology Eindhoven).

These clusters include in total more than 100 HiSPARC stations. From these around 75 are operational [2].

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The data rate measured by HiSPARC cosmic ray detectors fluctuate over time. We assume that these fluctuations are due to variations in environmental variables such as pressure and air temperature at sea level. This is formulated more precisely in my research question (section 3.5). In this thesis I report on the development of a model that describes the fluctuations observed by detection stations located at Science Park Amsterdam.

In order to investigate the influence of weather conditions on HiSPARC cosmic-ray measurements some HiS- PARC detection stations are equipped with a commercial weather station. Such a weather station is installed next to HiSPARC detection station 501 at Science Park. It is described in section 4.

Shower data from our cosmic-ray detectors is automatically send to the HiSPARC database using a LabVIEW software interface. At the start of my research project this was not the case for weather data. Although a rudimentary version of the weather software was available my first task was to make weather data acquisition possible using LabVIEW software. Currently, eight HiSPARC high schools¹send their weather data to the local HiSPARC database using this software:

1. Het Amsterdams Lyceum (station 3) 2. Zaanlands Lyceum (station 102) 3. Nikhef (station 501)

4. Leiden University (station 3001)

5. CSG Prins Maurits Middelharnis (station 3201) 6. Eindhoven University (station 8001)

7. Pius X College Bladel (station 8005)

8. Stedelijk College Eindhoven (station 8006) [3].

The weather station in HiSPARC station 501 located at Science Park Amsterdam became operational at 23 May, 2011 and has been collecting weather data since. In the analysis described in this thesis these data are used. The development of the weather station software is described in section 4.1.

The second part of my project was formed by data analysis. In order to do that I had to learn the programming language Python. My principal goal was to search for correlations between shower and weather measurements and with these correlations explain the observed fluctuations in the shower measurements. The methods I used for my shower and weather data analysis are described in chapter 5. The models I developed to describe the observed fluctuations of shower data are outlined in chapters 6 and 7.

A secondary goal was the development of analysis software for use by high school students. Currently, the students can access shower measurements for every hour via the HiSPARC website. Data analysis can be done with Excel. I developed additional analysis software that enables high school students to download shower and weather data, plot the data and perform a correlation analysis between variables. Python runs in the background. Apart from knowledge of basic commands high school students do not have to master the Python language in order to use the software. Now this barrier has been removed, it has become possible to perform data analysis in the classroom.

The software is described in appendix A.

1Last check at July 1, 2012

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Chapter 2

Cosmic rays

2.1 Appetizer: history of cosmic rays

Around 1900 a theoretical problem in physics was identified: electroscopes discharged faster than expected. An electroscope is an device for detecting electrical charge (figure 2.1).

Figure 2.1: An electroscope [4]

When you charge the electroscope, the charge is conducted through the metal onto the leaves at the bottom.

The leaves will possess charge of the same sign and therefore they will repel each other. Eventually, the charge will leak off and the leaves will come together again. At the end of the nineteenth century, the knowledge that material consists of atoms was established well enough to explain the spontaneous leak of charge. In 1897 J.J. Thomson discovered the electron in his cathode ray experiments and Millikan indicated the electron in 1909 as the unit of electric charge. The discharge of the electroscope was explained by the idea that the molecules of a gas could be ionized. If the leaves were negatively charged, they would attract positive ions. The ions balance the charge of the leaves and in this way the leaves come together (vice versa for positively charged leaves). This still left the question unanswered of how this ionization of the air around the leaves took place. By discoveries of R ¨ontgen in 1895 (X-rays), Becquerel in 1896 (radioactivity), and the Curies in 1898 (radium) it became clear that radioactive materials produced ionizing radiation and therefore in fact could cause the discharge of the electroscopes. This could partly explain the rapid discharge. The materials of the electroscope could be slightly radio-active and therefore be responsible. This effect could be minimized by shielding the leaves with water and lead. It appeared that the shielding had no effect, therefore, some radiation had to come from an external source. It became the dominant view that the rapid discharge was caused by radiation from radioactive materials in the outer layer of the Earth.

This idea could be tested by setting up electroscope experiments at different altitudes and measure the time it would take to discharge from the same amount of charge. The radiation should become weaker, when the experiment is

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carried out farther from the surface of the earth. These experiments were conducted by Theodor Wulf who took his electroscope to the top of the Eiffel tower in 1910 and by Gockel who took his electroscope in a hot air balloon. They concluded from their results that the rate at which their electroscopes discharged did not decrease with altitude, or not as fast as they expected.

Figure 2.2: Victor Hess in his balloon [5]

In 1912, Hess made several balloon flights and measured the discharge of his electroscope at various altitudes.

He noticed, as Gockel had done earlier, that up to a height of 1100 meters no essential variation in the discharge time could be observed. On the morning of August 7, he took off from Außig in Germany to land 200 kilometers farther close to Berlin. During this flight, he measured the discharge at a height of 5350 m. After he had collected data from more than thirty flights he noticed something unexpected. Not only did the radiation not decrease as expected, at heights above the 2000 meters he even measured an increase in the radiation. From this he drew the conclusion that

“The discoveries revealed by the observations here given are best explained by assuming that radiation of great penetrating power enters our atmosphere from the outside.” [6]. This conclusion was not shared by everyone in the physics community. Robbert Millikan was the most critical. He carried out a series of experiments and convinced himself and anyone else who had any doubts about the claim made by Hess. Moreover he coined the name ‘cosmic rays’ [7] (p. 1–25).

2.2 Composition of primary cosmic rays

The Earth’s atmosphere is continuously bombarded by cosmic rays. The composition of these so called primary cosmic rays is estimated to consist of 89% protons, 10% alpha particles and 1% heavier nuclei and electrons. The tiny fraction of heavier nuclei consists of mainly lithium, beryllium and boron nuclei [8] (p. 278).

2.3 Energy spectrum

The energy spectrum of the primary cosmic rays is displayed in figure 2.3. The flux Φ (in m⁻²s⁻¹ sr⁻¹GeV⁻¹) is plotted against the energy E (in eV) per cosmic-ray particle. The energy spectrum above 10⁹eV as displayed in

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figure 2.3 can be described by a power law. More explicitly the flux density Φ has the form:

Φ = aE^−b (2.1)

with E the energy of the cosmic rays (in eV) a and b are numerical constants. In figure 2.3, b has has a value of ' 2.7 in the range 10⁹ eV < E < 10¹⁵eV. At E ' 10¹⁵the spectrum steepens. This point is called ‘the knee’. b has the value of 3.1 for the range 10¹⁵eV < E < 10¹⁹eV, where the spectrum flattens. This point is called ‘the ankle’.

Note that the cosmic-ray energy spectrum ranges over more than thirteen orders of magnitude. Moreover, the flux ranges over thirty orders of magnitude. When the energy increases with a factor ten, the flux drops roughly a factor 10³. This is huge. It means that we detect a particle with an energy of ' 10¹¹eV once per second per square meter.

However, we detect a particle with an energy of ' 10¹⁸eV once per year per square kilometer! [9]

Figure 2.3: The cosmic-ray energy spectrum [9].

The unit of flux needs some clarification. Usually, flux is the number of particles passing through a surface area per second (s⁻¹m⁻²). The cosmic-ray flux is also given per space angle. This space angle in steradian (sr⁻¹) is defined analogous to the way the radian is defined. In a circle the radian is defined as the angle where the arc length is equal to the radius of that circle (figure 2.4 left). A circle has circumference 2πr. One turn around a circle from the center is therefore equal to an angle of 2π radians. For a sphere the steradian is the space angle where the

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Figure 2.4: The definition of the radian and the steradian [10].

surface area of that sphere is equal to the radius of the sphere squared (figure 2.4 right). The surface area of a sphere is given by 4πr². The space angle of a sphere is therefore equal to 4π steradians [10]. Finally, the flux is also given per energy bin (GeV⁻¹).

Cosmic rays with energies ranging from 10⁷-10¹⁰eV mostly originate in the Sun and are therefore called ‘Solar Cosmic Rays’. Cosmic rays with energy smaller than 10⁷eV are stopped by interactions in the Van-Allen belts of the Earth. Therefore, most of them do not reach the Earth. This is why a flattening in the energy spectrum towards lower energies is visible. Particles with energies above 10¹⁰eV are called ‘Galactic Cosmic Rays’ and are thought to originate in our own galaxy. Above 10¹⁵ eV (the ’knee’ in the energy spectrum) is the regime of the ‘Ultra- High Energy Cosmic Rays’ (UHECRs). In theoretical models these cosmic ray particles are accelerated in pulsars or shockwaves in supernova remnants [11] (p. 1-12) The most energetic particle ever detected has an energy of 3.0·10²⁰ eV [12]. Before the development of particle accelerators in the 1930s cosmic rays were the only source of energetic particles available. Our most powerful accelerator, the LHC, has an energy limit that lies at 10¹²− 10¹³eV. This is much lower than the highest energy cosmic rays we have observed. Therefore, to understand the highest energy particles, cosmic ray research is indispensable.

2.4 Sources of UHECRs

Current models describing possible sources for UHECRs can be divided into three categories. The first category consists of the so called ‘bottom up’ models. In these models low energy particles are accelerated up to high energies by energetic astrophysical objects. The acceleration takes place either in shockwaves by a process called first order Fermi acceleration, or the particles are accelerated by varying electromagnetic fields. The second category consists of the so called ‘top down’ models. In top down models cosmic-ray particles originate from the decay of supermassive particles (often called X particles), with energies > 10²⁰eV, that originated in very high energy processes in the early Universe. Finally, the third category consists of so called ’hybrid’ models, which are combinations of the first two categories.

2.4.1 The Hillas plot

Figure 2.5 contains the so-called Hillas plot which shows the distribution of possible cosmic ray sources as function of both the magnetic field strength and the size of the accelerator. The relation between these quantities can be derived as follows. One can make rough estimates for the possible energies an accelerator can achieve. A charged particle that moves in a magnetic field will rotate in a circle with radius r given by:

r =m₀γv

ZeB (2.2)

with m0the particles rest mass, γ the relativistic Lorentz factor, v the velocity of the particle perpendicular to the magnetic field, Z its atomic number, e the elementary charge, and B the magnetic field strength [8] (p. 331-333). The maximum attainable energy for a particle accelerated by Fermi acceleration can be estimated. As long as a particle remains in the acceleration zone, it can acquire more energy. When the particle leaves this zone, i.e. if the particle

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takes a path with a radius larger than the Larmor radius, the particle will have the maximum energy that can be acquired in this accelerator. In the following calculation we will let the Larmor radius of the particle approach the size of the acceleration zone to get an expression for the maximum energy acquired by a particle, traveling in a medium with magnetic field B. We start with formula (2.2) of the Larmor radius. The energy of the particle is:

E = γmc² (2.3)

where γ is the Lorentz factor, γ = (1 −^~^v·~_c2^v)⁻¹², m is the mass of the particle, and c is the speed of light. With formula (2.3) and β = ^v_c we can rewrite the Larmor radius (2.2) and solve for the energy E:

E = ZeBrc

β (2.4)

The acceleration zone will have length of order 2r; substituting this in formula (2.4) and using the fact that β ' 1, we obtain

Emax∼ 2ZeBrc (2.5)

This expression for Emax is only an estimate of the maximum energy for a particle traveling in a medium with magnetic field B. It is sometimes called the ’Hillas criterion’, after A.M. Hillas who first made this analysis in 1984.

The maximum attainable energy for a particle accelerated through the magnetic field generated by a pulsar can also be estimated. One of Maxwell’s equations in free space reads (in Gaussian units):

∇ × ~E = −1 c

d ~B

dt (2.6)

where ~Bis the magnetic field of the pulsar, ~Ethe generated electric field and c the velocity of light.

From a dimensional analysis we can write formula (2.6) as:

E R ' B

cP ⇒ E =BR

cP (2.7)

with P the period of the pulsar and R its radius. We want to formulate an expression for the maximum energy E_maxto which a particle can be accelerated by a pulsar. Energy E is charge Ze multiplied by an potential difference V:

E = ZeV (2.8)

We can write a potential difference in terms of the electric field. The electric field can be described in units of [^V_m].

The radius of the pulsar has units of length. Thus the electric field multiplied by the radius of the pulsar will have units of a potential difference. Therefore the expression for the maximum energy Emax, to which a particle can be accelerated, will be of the form:

Emax= ZeRE (2.9)

If we substitute formula (2.7) into the expression for the maximum energy, formula (2.9) using P = ^2π_ω we obtain:

Emax' ZeR²Bω

c (2.10)

where we indicate with ' that this is only an estimate for the maximum attainable energy of a particle accelerated by the magnetic field of a pulsar. From equations (2.5), (2.10) and equations based on similar reasoning, we can

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Figure 2.5: In the Hillas diagram the magnetic field strength of possible acceleration sites is plotted against the accelerator size [13], (see text)

make the plot in figure 2.5. In this figure, the logarithm of the magnetic field strength (in Gauss) is plotted against the logarithm of the accelerator size (in kilometers). The line labeled ‘protons (1 ZeV)’ indicates that in the area above this line protons can not be accelerated to an energy larger than 10²¹eV. Similarly, the line labeled ‘protons (100 EeV)’ indicates that in the area above this line protons cannot be accelerated to an energy larger than 10²⁰eV.

The line ‘100 Fe EeV’ indicates that in the area above this line iron nuclei cannot be accelerated to an energy larger than 10¹⁹ eV. The diagram shows that if you want to accelerate particles to a high energy you need an enormous acceleration zone, or a very large magnetic field, and ideally both. The higher the energy of the cosmic ray, the more energetic the source must be (or the primary is accelerated even more along its way towards us). For the highest observed cosmic-ray energy (∼ 10²⁰eV) an accelerator is needed that reaches energies of at least 10²¹eV.

As these accelerators stand at a larger distance from Earth the sources need to be even more energetic because the particles loose energy while traveling towards the Earth in interactions with interstellar matter. Moreover, charged particles will be deflected in interstellar magnetic fields. The traveled distance therefore becomes even larger, and the accelerator must be of an even higher energy, or must be at a closer distance. This forms a big problem. Radio galaxies and active galactic nuclei can only produce particles with energies a factor 100 smaller than the observed UHECRs. Looking at our options we need to bring up more energetic candidates that can account for these high energies [14] (p. 16-17), [15] (p.19-21), [16], [17], and [18].

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2.4.2 The GZK energy limit

Greisen, Kuzmin and Zatsepin showed that an upper limit should exist on the energy of the UHECRs, because UHECRs with energies above 5 · 10¹⁹eV will interact with the microwave background radiation (MBR). Around 337.000 years after the Big Bang, the Universe was cool enough for recombination to occur: hydrogen and helium nuclei captured electrons to form neutral atoms. From this point in tim, the Universe has become transparent because the photons practically stopped interacting with charged matter. These photons are known as the microwave background radiation. The photons are cooled due to the expansion of the Universe and are present everywhere in the Cosmos with a characteristic black body spectrum that corresponds to a temperature of 2.7 K [19]. In the rest frame of the cosmic rays these MBR photons seem to be high energetic gamma photons. For UHECRs these gamma rays can have enough energy for pion production:

γM BR+ p → 4⁺→ n + π⁺ (2.11)

γ_{M BR}+ p → 4⁺→ p + π⁰ (2.12)

These interactions¹take place until the UHECR do no longer have enough energy for pion production (' 140M eV ).

UHECR-photons can lose energy through pair production²:

γM BR+ γ → e⁻+ e⁺. (2.13)

The energy spectrum for UHECRs is shown in figure 2.6 measured by HiRes and AGASA. In this figure the flux is multiplied by E³to make the changes more apparent. Not many cosmic rays have been detected above the GZK- limit (in figure 2.6 we can see a few events above the GZK-limit). If the GZK limit exist the sources where UHECRs (with energies above the GZK limit) originate must be relatively close by. A proton with an energy of 10²⁰eV has a mean free path around 10 Mpc. After 50 MPc the energies of UHECRs should have dropped below the GZK-limit.

This limits the location of the sources of these UHECRs to our Virgo Supercluster [20] (p.294).

Figure 2.6: The UHECRs energy spectrum [21] (p. 14)

1The cross section for these processes is around 250 microbarns [8] (p.340).

2The cross section for this process is around 0.01 barn. This is larger than the cross section for pion production. However, the pion process consumes more energy because the rest mass of the pion is so much larger than the rest mass of the electron [8] (p.341).

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2.5 Air shower development

Figure 2.7: Schematic diagram of the development of a shower in the atmosphere [22].

As mentioned in section 2.2 the incoming primary cosmic rays consist for 89% out of protons, 10% alpha particles and 1% heavier nuclei and electrons. If these charged particles enter the upper atmosphere, the primary cosmic rays collide inelastically with an individual nucleon in an air-particle nucleus (probably from an oxygen or nitrogen atom) and they will interact. The point of first interaction for a proton is on average around 15 km above sea level (figure 2.9). In this strong interaction between the quarks of the particles new particles are produced (mostly pions and also kaons because they are the lightest particles made out of quarks). These are called secondary particles. If these secondary particles still have enough energy they will generate new particles through collisions. In this way an air shower will form and will produce more particles with every interaction, which is displayed schematically in figure 2.7. This process continues until the energy per secondary particle drops below the pion’s rest mass (' 140M eV ). At a certain depth Xmax the number of particles in the shower will be at a maximum. After this maximum the number of particles decreases. This is displayed in the longitudinal shower profile (from the point of first interaction to sea level) of figure 2.8b. The lateral shower profile (away from the shower core) for sea level is displayed in figure 2.8a. If many of the secondaries reach ground level this is called an Extensive Air Shower (EAS) of particles [23].

Mesons are not stable particles and will decay via the decay modes listed in table 2.1. Only the most probable modes are shown along with the branching ratio. Many pions and kaons decay during their flight since they have mean lifetimes of the order 10⁻⁸s, whereas the muon has a lifetime of 2.2 · 10⁻⁶s. With γ > 20 a muon will reach the earth’s surface. From the decay modes in table 2.1 it therefore follows that at sea level, we are mainly left with electrons, muons and photons [26].

2.5.1 Particle flux and composition at sea level

The particle fluxes measured at sea level altitude are listed in table 2.2. Of the electron flux, 40–50 % are positrons, above 0.1 GeV (but only ∼ 5% at 1 MeV) For the photon flux, these values are theoretical values as measurements are inadequate [27].

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Decay Mode Branching ratio

π⁺→ µ⁺+ νµ 99.98770 ± 0.00004 %

π⁻→ µ⁻+ νµ 99.98770 ± 0.00004 %

π⁰→ γ + γ 98.823 ± 0.034 % π⁰→ e⁺+ e⁻+ γ 1.174 ± 0.035 %

K⁺→ µ⁺+ νµ 63.55 ± 0.11 % K⁺→ π⁺+ π⁰ 20.66 ± 0.08 % K⁺→ π⁺+ π⁺+ π⁻ 5.59 ± 0.04 % K⁺→ π⁰+ e⁺+ ν_e 5.07 ± 0.04 % K⁺→ π⁰+ µ⁺+ νµ 3.353 ± 0.034 % K⁺→ π⁺+ π⁰+ π⁰ 1.761 ± 0.022 %

K⁻modes are charge conjugates of the decay modes above.

µ⁺→ e⁺+ ν_e+ ν_µ ∼ 100 %

µ⁻→ e⁻+ ν_e+ ν_µ ∼ 100 %

Table 2.1: Decay modes of secondary particles in a shower [26]

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Figure 2.8: (a) Lateral and (b) Longitudinal shower profile for an incoming proton with energy of 10¹⁹eV [24].

Figure 2.9: Schematic diagram of the development of a shower in the atmosphere. [8] (p.149), [25].

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E (GeV⁻¹) Imuon Ielectron Iphotons Iprotons

0.001 100 60 130 2.1

0.01 100 28 60 2.1

0.02 100 20 40 2.1

0.1 99 6.0 8 1.9

0.2 97 3.0 3.5 1.5

0.5 86 1.0 1.1 0.9

1 69 0.38 0.37 0.51

2 46 0.12 0.11 0.25

5 20 0.02 0.02 0.077

10 8.6 - - 0.025

20 3.0 - - 0.008

50 0.58 - - 0.0016

100 0.14 - - 4.3 · 10⁻⁴

200 0.03 - - 1.1 · 10⁻⁴

500 3.2 · 10⁻³ - - 2.0 · 10⁻⁵

1000 5.0 · 10⁻⁴ - - -

Table 2.2: The secondary particle flux detected at sea level (in m⁻²s⁻¹sr⁻¹)

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Chapter 3

Cosmic-ray detection

A HiSPARC detector station consists of two or four individual cosmic ray detectors. A single detector consists of a scintillation plate, a light guide and a photomultiplier tube (figure 3.1).

Figure 3.1: Schematic diagram of the HiSPARC detection setup. [28].

The scintillation material is a plastic doped with fluor. If a charged particle (e.g. a muon or an electron) with sufficient energy traverses the scintillation plate it brings fluor atoms in an excited state. The energy is re-emitted in the form of photons. These photons travel through the scintillator plate and the light guide until they reach the photomultiplier tube (PMT). The PMT converts the photon signal into an electric signal [29] (p.174). In a two detector setup, the detectors are placed 6–8 meters apart (figures 3.2 and 3.3). The four detector setup is placed in an equilateral triangle (figures 3.2 right and 3.4). Along with the detectors a GPS antenna is placed to acquire an accurate timestamp for every event.

3.1 Energy loss in a scintillator

When a charged particle traverses the scintillation plate a fraction of its total energy is transferred by ionizing the material. For a muon the energy loss is in the order of 1h. Charged particles loose their energy by ionizing the medium. The energy loss is expressed by the so called stopping power ^dE_dx of the medium. This is the energy a particle has lost after traversing 1 cm of material. By convention the stopping power is expressed not in terms of

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Figure 3.2: Schematic diagram of a set of two (left) and a set of four (right) HiSPARC detectors on a rooftop. [30]

(p.9 – 10).

Figure 3.3: A set of two HiSPARC detectors on a rooftop.

[30] (p.7).

Figure 3.4: A set of four HiSPARC detectors (station 501) on a rooftop.

length x, but in terms of traversed mass per unit of cross sectional area (X). This way of describing length needs some clarification [8] (p.51).

Mass per unit area A centimeter of air will absorb less radiation than a centimeter of water, because the densities of the materials differ (ρwater/ρ_air ≈ 800). However, if the particle travels through 8 meter of air the energy loss becomes comparable with the loss in 1 cm water. Hence, for the energy loss we have to take both the density ρ of the material and its thickness h into account. As a consequence it is customary to describe the energy loss in a

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radiation absorber in a way independent of the density of the material.

Figure 3.5: Definition of mass per unit area [7] (p.234), (see text).

Consider a prism with 1 cm²of cross sectional area that is cut from a radiation absorber with thickness h (figure 3.5). The mass of this prism is the mass per unit area ( g / cm² ). For constant density this mass is the volume (1 · h = h) times its density ρ. More formally the so called interaction depth X is defined as the traversed mass per unit cross sectional area.

X = Z

ρ(h) dh (3.1)

This definition is independent of the traversed material and allows for a comparison of the absorbing effects of a passage through lightyears of interstellar medium, to a passage through a few centimeters of scintillation material [7] (p.234–235), [31] (p.18-19) [32].

Bethe–Bloch formula Using the mass per unit area the stopping power of material _dX^dE has unit ^{M eV}^g

cm2

and is described by the Bethe Bloch formula:

−dE

dX = K · z²Z A

1 β²

1

2ln 2m_ec²β²γ²Tmax

I²

− β²−δ(βγ) 2

(3.2) with z the charge number of the incident particle, Z the target charge number, A the target mass number, β = ^v_c the relative velocity parameter, γ =√ ¹

1−β², Tmaxthe maximum kinetic energy transfer in one collision with an electron, Ithe average ionization energy of the target and δ the so called density correction. The constant K is given by:

K = 4πN_Ar_e²m_ec² (3.3)

with NA avogadro’s number, re the classical electron radius, mec² the electron rest energy. The minus sign in

−_dX^dE makes the stopping power a positive number. Moreover, the minus sign indicates that a particle loses kinetic energy. In figure 3.6 the stopping power _dX^dE of electrons and muons in a polyvinyltoluene scintillator is shown.

The stopping power in units of ^{M eV}^g

cm2

is plotted against βγ. βγ is used to make the plot independent of the type of particle¹. The minimum stopping power lies around βγ ≈ 3.5. For electrons this corresponds to 1 MeV and for muons this corresponds to 325 MeV. These are called minimum ionizing particles (MIPs). The Bethe Bloch formula describes the average energy loss, but not the energy loss for an individual particle. Not every charged particle that traverses a scintillation plate will lose the same amount of energy. If the particle comes in close encounter with the atoms in the material it will lose more energy and vice versa. This results in statistical fluctuations of the mean energy loss. The fluctuations are described by a so called Landau distribution. An example of such a distribution is shown in figure 3.7 [33] (p. 51–53), [23].

1The momentum p of a particle is given by: p = γm0βchence βγ = _m^p

ocwith m0the rest mass of the particle.

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Figure 3.6: Stopping power of electrons and muons in a polyvinyltoluene scintillator. [23].

Figure 3.7: Landau distributed energy loss. Figure adapted from [34] (p. 9).

3.2 Photomultiplier

A photomultiplier tube (PMT) converts a photon signal into an electric signal. A PMT consist of a photocathode, several electrodes called ‘dynodes’ and an anode (figure 3.8). If a photon strikes the photocathode an electron will be emitted by means of the photoelectric effect. Due to an applied high voltage the electron is accelerated towards the first dynode. When the electron hits the dynode with its increased kinetic energy it transfers some of its energy to the electrons of the dynode. This results in secondary emission of several electrons. To every following dynode a higher voltage is applied than the previous one. This causes the electrons to be accelerated towards every next dynode. This acceleration and emission of electrons continues until sufficient charge hits the anode to give a current of several millivolts that can be measured.²The signal is send via the readout electronics to a computer [35] (p.169).

The performance of the photomultiplier is influenced by its ambient temperature. The dark current (a small current caused by electrons emitted by the photocathode unrelated to the photon signal) increases with temperature.

This background is suppressed in the HiSPARC detector by measuring only coincidences. The photocathode is sensitive to temperature too. Leo [35] claims that this is explained by the fact that the Fermi level and resistance of the cathode will change with temperature. The specifications of the photomultiplier mention a temperature coefficient of −0.5%^◦C⁻¹of the PMT gain (amplification factor). A higher temperature will mean less photons that are emitted at the cathode, less charge will reach the anode and we will measure a lower signal [35] (p.196), [37], [38].

2The name photomultiplier is not well chosen, since the device multiplies electrons, not photons. An ‘electronmuliplier’ might be a better name.

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Figure 3.8: Schematic diagram of a photomultiplier tube. [36].

3.3 Electronics and trigger conditions

The analog pulses coming from the PMTs have to be digitized to allow for signal analysis using a computer. To analyze the signal with a computer. This is done by connecting each PMT with a HiSPARC electronics box. Each box has two inputs that contain four 12-bit ADCs (each channel has two ADCs). The ADCs measure voltages in the range 0 to 2 V. In figure 3.9 the electronic boxes are shown for a four detector setup. The two boxes act as a master-slave unit. A single box communicates with two cosmic-ray detectors (PMT 1 and PMT 2). The data is received (input) and the high voltage at the PMT-base can be adjusted (control).The master box also has an input for the signal from the GPS antenna (this input is situated on the back of the master box and is therefore not shown in figure 3.9).

Figure 3.9: HiSPARC II version of the electronics. [28].

If the incoming signal meets certain trigger conditions (section 3.3) the ADC is read, along with a GPS time stamp and send to the HiSPARC computer through USB. An electronics box is equipped with a FPGA circuit (Field- Programmable Gate Array ). With this FPGA a user can control parameters such as the trigger levels, the high voltages of the individual PMTs and the coincidence time window. An electronics box has an internal memory that can only store the data of a few events. The computer can store events locally and sends data to the HiSPARC server. This means that if the computer is turned off or restarted, data is lost [39] (p.5).

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Trigger conditions Secondary particles that are produced in the same shower arrive approximately at the same time at the detector. To make sure we only measure those particles, we use another scintillation detector (or more) in coincidence mode. This means that the electronics only registers an event if one (or more) detectors give a signal within a time window of 1.5 µs. We also want to make sure that a signal from the PMT corresponds to a real particle and not to some coincidental emission of electrons. Therefore, the electronics registers an event only if the amplitude of the pulse is above 70 mV . With this trigger condition a single scintillation detector has a detection rate of about 120 Hz (this includes signals from particles that do not come from showers). For two detectors placed 6–8 m apart we measure an event rate around 0.3 Hz. This means that two signals were registered above 70 mV , within a time window of 1.5 µs. For a four detector station we require two signals above this high threshold of 70 mV or three signals above 30 mV . This results in a trigger rate of about 0.7 Hz. With these trigger conditions the probability that these coincidences are not due to particles that originate from the same shower is close to zero [23].

Figure 3.10: HiSPARC data flow [40].

HiSPARC data flow Shower data and weather data is send from the HiSPARC computer to the HiSPARC database.

Three different detector types can be stored, each with its own table format::

• shower detector (see section 3.4)

• weather station (see section 4)

• lightning detector

It is speculated that there is a correlation between lightning strikes and the amount of secondary cosmic-ray particles. With the HiSPARC detectors, it might be possible to measure the correlation. Currently a lightning detector from the company Boltek is made operational at Nikhef. This detector registers the angle and the distance from the lightning strike. There are plans to place lightning detectors in Groningen and Eindhoven too. With these detectors operational, it will become possible to triangulate the position of the lightning strike

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(the distance to the strike, from a single detector, has a relative large uncertainty, in this way the uncertainty of this measurements can be greatly reduced).

3.4 Shower data acquisition

On the HiSPARC computer the software ‘HiSPARC II DAQ’³is used to control the electronics boxes. The graphical user interface (GUI) is written using LabVIEW. HiSPARC DAQ (figure 3.11) enables you to read the electronics boxes and to control the FPGAs. Via the GUI, the user can change settings such as the thresholds and the high voltages of the individual PMTs. One can also look at the detector response. An example is shown in figure 3.12. Here two pulses are visible. These pulses are the result of photons generated in the scintillation plate that are converted into an electronic signal by the PMT and send through the electronics where it is finally digitized by the ADC. Along the way the pulse width broadens. We can see in both signals a second dip after the large dip which indicates that probably more than one particle traversed the scintillation plate.

The minimum value of the signal is the pulse height of an event (figure 3.12). The pulse height has a negative value because of the negative charge that reaches the anode of the PMT. The pulse is registered in ADC counts. Raw ADC units can be converted to millivolts units by V = −0.57x + 113. This unit is used on the vertical axis of figure 3.12. The shower data that is processed by LabVIEW and send to the HiSPARC database is listed in table 3.1.

Figure 3.11: Screenshot of HiSPARC II DAQ 3.04 the Data acquisition software.

Figure 3.12: Detector response. Two PMTs produced a signal.

3.4.1 Pulse heights

The minimum value of the pulse registered by the ADC is the pulse height of an event (figure 3.12). If we gather enough data a pulse height histogram can be plotted. On data.hisparc.nl the pulse height histogram for a day is displayed (figure 3.13).

For a four-detector station it is important to realize that the trigger condition is satisfied for every event (two pulse heights above 70 mV or three pulse heights above 30 mV within an interval of 1.5 µs) and not for every registered pulse height. If the trigger condition is met all four PMTs are read. If two pulse heights were above 70

3This software was developed by Jeroen van Leerdam et al.

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Variable (s) Description Unit

timestamp Unix timestamp in GPS time. seconds

extended timestamp Unix timestamp in GPS time. nanoseconds

nanoseconds part of the timestamp nanoseconds = extended timestamp minus (timestamp · 10⁹)

nanoseconds data reduction True or False if LabVIEW erased some

of the data of the trace.

- trigger pattern Binary number representing the trig-

ger conditions.

-

baseline Reference point for the trace of every

plate ( 200 ADC counts).

ADC counts

standard deviation of baseline - ADC counts

number of peaks The number of peaks in the trace deter- mined by LabVIEW

- pulse heights Maximum pulse height of the trace mi-

nus the baseline.

ADC counts

integrals Integrated trace. ADC counts nanoseconds

event rate Triggered events per second averaged

over the last 90 seconds

Hz

Table 3.1: Variables stored in the events table of the HiSPARC database. [1].

Figure 3.13: Pulse height histogram of station 501 January 12, 2012. [41].

mV than the pulse heights measured by the other two PMTs do not have to be above 70 mV. This is how pulse heights below the lower trigger level get registered.

Not all photons that are generated in the scintillation plate will reach the photomultiplier. The position of impact will determine the distance traveled by the photons through the scintillation plate to the PMT as can be seen in figure 3.14. If the traversed distance and/or the number of reflections increase, less photons will reach the PMT. This can be due to absorption or because the photons do not arrive within the coincidence interval of 1.5 µs [42] (p. 83-84).

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The electrons and muons that traverse a scintillation plate are minimum ionizing particles (MIPs). The energy loss of these MIPs in the scintillator are therefore Landau distributed (look back at figure 3.7). If a MIP loses more energy in the scintillation plate more photons will be produced and we will observe a higher pulse height. Therefore the pulse height will be proportional to the energy loss of the MIP and we expect the pulse heights of the MIPs to be Landau distributed too. The peak visible in figure 3.13 is called the Most Probable Value or (MPV) of the pulse heights. It does not look exactly like a part of the Landau distribution shown in figure 3.7. This is because we have to take other factors into account. More on this is described in section 5.3 [1].

Figure 3.14: Three possible photon paths through a HiSPARC detector. Despite of multiple reflections the left photon signal will not reach the PMT, whereas the other two will reach the PMT. [42] (p. 83).

3.4.2 Event rate

If the trigger conditions are met we have an event, the PMT registrations are saved and HiSPARC DAQ automatically calculates the event rate (in Hz) as a running average for the last 90 seconds. Sometimes the HiSPARC detector computer is rebooted. If this happens no data can be send to the database. If the computer was turned off the event rate becomes zero and then slowly increases to the actual value for the event rate. On data.hisparc.nl the number of events per hour is plotted for every day.

Figure 3.15: Event rate of station 501 January 12, 2012. [41].

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Poisson statistics

Cosmic-ray events occur at random times at a constant rate. Moreover, we assume that the signals produced by different cosmic rays are independent of each other. This makes sense since the time between events (around a second for a 4-plate station) is much larger than the duration of the signal (around 6 µs). Since these requirements are met the probability Pkof observing exactly k events in a given time interval is given by Poisson statistics:

P_k =λ^k

k!e^−λ (3.4)

with λ the expected number of events in the chosen time interval and e Euler’s number (2.71...). k has to be an integer or zero (we cannot measure 1.2 events). The mean value is equal to λ and the variance is also equal to λ.

So if we observe N events we expect the Poisson distributed events to fluctuate around N with standard deviation σ =√

N[43], [44]. If the number of events increases the Poisson distribution starts to look like a normal distribution, as can be seen from figure 3.16. We can use a normal distribution as an approximation of the Poisson distribution to fit our data.

Figure 3.16: For increasing occurences k the poisson distribution begins to resemble a normal distribution.

As a rule of thumb this approximation can be used if the number of events k is larger than 20 [45]. In figure 3.17 the number of events in a time interval of 90 seconds for a whole day (July 21, 2011, station 501) is plotted in a histogram. On the y-axis the normalized number of events Nnormis shown:

N_norm= N_bin Ntotal· dbin

(3.5) with Nbinthe number of events in a bin, Ntotal the total number of events and dbin the bin width. If this normal- ization is used the integral under the histogram is equal to 1 [46]. Now a normal probability density can be fitted to the histogram (dashed red line of the data in the histogram in figure 3.17). We see that the standard deviation is approximately given by√

N:

σ_measured= 7.919 (3.6)

σ_theory=√ N =√

63.57 = 7.973 (3.7)

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Figure 3.17: Histogram of the number of events per 90 seconds for station 501 on July 21, 2011 (green bars) along with a Gaussian fit (red dashed line).

3.5 Research question

Figure 3.18: Event rate fluctuations (for one hour intervals) of detection station 501 at Science Park Amsterdam.

In figure 3.18 the fluctuating number of events per hour for detection station 501 is shown. The most probable value (MPV) of the pulse heights registered by HiSPARC cosmic-ray detectors fluctuates too. In figure 3.19 the fluctuating MPV-value for plate 1 of detection station 501 is shown (calculated for three hour intervals).

Ideally we want our detection setup not to be sensitive to large fluctuations. The Earth is bombarded with cosmic rays at a constant rate, so ideally large fluctuations or a periodic pattern in the event rate should not exist.

In figure 3.18 the event rate is clearly not constant. Moreover, most secondaries that traverse our detector have approximately the same mean energy therefore the energy loss in a scintillation plate for these ionizing particles

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Figure 3.19: MPV fluctuations of the pulse heights (for three hour intervals) registered by scintillation plate 1 of detection station 501.

(e.g. an electron or muon) should also be nearly constant. Therefore the MPV of the pulse heights should not fluctuate this much. However, this is not the case.

Since we do not expect that these fluctuations are due to the secondary particles themselves we can look at environmental factors such as weather conditions. To investigate the influence of weather conditions on HiSPARC cosmic ray measurements some HiSPARC detection stations are equipped with a professional Vantage Pro or Van- tage Pro 2 weather station. This device is described in section 4. Shower data is automatically send to the HiSPARC local database through a LabVIEW interface. For weather measurements this was not the case. Therefore the first part of my research project was formed by the development of such a LabVIEW interface (the software is described in section 4.1). The weather station software is now operational and since 23th of May, 2011 data is collected at Science Park Amsterdam, connected to detection station 501.

In the second part of my thesis research project I developed linear models that describes the fluctuations of the number of events per hour and the MPV of the pulse heights (calculated for three hour intervals). In my models the observed fluctuations are described using atmospheric variables.

In order to conduct this analysis I had to learn the programming language Python. My goal was to search for a correlation between shower and weather variables and with this correlation explain the observed fluctuations in the shower measurements.

Research question

To what extent do weather circumstances influence cosmic-ray data measured with HiSPARC cosmic-ray detectors?

Subquestions

• Is there a correlation between the event rate measured per hour by HiSPARC cosmic-ray detectors and weather variables?

• Is there a correlation between the most probable value of the pulse heights (for three hour intervals) measured by HiSPARC cosmic-ray detectors and weather variables?

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A secondary goal was the development of analysis software intended for use by high school students. Cur- rently, students can access shower measurements for every hour via the HiSPARC website and data analysis can be done with Excel. I developed analysis software that enables high students to download shower and weather data, plot this data and perform a correlation analysis between variables. Python runs in the background. Apart from knowledge of basic commands high school students do not have to master the Python language in order to use the software. Now this barrier has been removed, it has become possible to perform data analysis in the classroom. The basic structure of the correlation analysis software is described in appendix A. The software can be downloaded from [47] or from Github [48]. The software consists of several Python scripts that can be modified if the user wishes to do so. They can also serve as examples for the analysis high school students can conduct on their own. For high school students I wrote a quick start guide which is included as appendix C.

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Chapter 4

Atmospheric conditions

Very soon after the discovery of cosmic rays it became clear that atmospheric conditions influence the number of secondary particles we detect at sea level. In 1926 Myssowsky and Tuwin [49] discovered time fluctuations that were attributed to pressure variations. This is called the barometric effect. These findings were confirmed with higher precision by Steinke in 1929 [50]. The barometric effect can be understood as an absorption effect. The atmospheric pressure is a measure for the air mass above the detector. The higher the pressure, the more material to stop the development of the shower and consequently, less particles are measured at sea level. This effect is enhanced by the higher altitude of the first collision of the primary cosmic ray particle in the atmosphere. Consequently, there is an anticorrelation between the event rate and the atmospheric pressure. The anticorrelation between the number of events per hour and the atmospheric pressure is shown in figure 4.1.

Figure 4.1: The number of events per hour and the atmospheric pressure measured by detection station 501 in July, 2011.

The temperature effect on the event rate was discovered about 10 years later. It is a much smaller effect than the barometric effect. Dorman [51] (p.10, 288) and James [21] (p.39, 42) offer the following explanation. If the

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temperature of the atmosphere becomes higher, the atmosphere will expand¹. Consequently, it was expected that less particles would reach sea level. However, a positive temperature effect was measured. This is because if the temperature rises, the upper atmosphere expands and its density will become less. Hence the amount of pions that are captured by air nuclei becomes less and more pions will decay into muons, leading to more events at sea level:

a positive effect. Hence the temperature effect is rather complicated. In addition to the temperature profile for the whole atmosphere, one has to know the distribution of pions, whereas HiSPARC only measure the temperature at sea level.

If we want to make corrections on the measured event rate we need to monitor atmospheric conditions by in- stalling a weather station near our cosmic-ray detection station. For this, the weather station Vantage pro and its successor Vantage pro 2 from Davis [52] were selected, which were installed at about twenty high schools and universities in the Netherlands six years ago. Shower data is send to the HiSPARC database with the use of LabVIEW software and python scripts. Davis developed its own software WeatherLink that enables you to display and store data on your computer. However, WeatherLink does not automatically enable us to do the same as the LabVIEW software. Therefore, it was decided to develop our own weather station software with LabVIEW by using a li- brary with functions (dll) that was made available by Davis. Floor Terra, a student at NIKHEF designed the first rudimentary version of the weather station data. In January 2010 I was assigned to complete his work. Moreover I developed a software installation manual that is included as Appendix B. The basic structure of the software is described in section 4.1. The software itself along with its documentation can be found at [47]. The LabVIEW source code can be downloaded from [53].

Weather station: Davis Vantage Pro 2 The Vantage Pro system consists of two parts: an Integrated Sensor Suite (ISS) that is installed on the roof (figure 4.2 below and figure 4.3) and a console (figure 4.2 above) that collects the data and sends it to the HiSPARC computer. There are wireless and cabled versions of the sensor suite. The wireless model is equipped with a solar panel. Unfortunately, it appeared that after six years some of the solar panels could no longer recharge the 3 volt battery in periods during which there was not much sunlight. Therefore it is recommended that the new purchased weather stations have to be cabled. At Nikhef, the Amsterdams Lyceum and the Zaanlands Lyceum we installed our own adapter with the non cabled systems. This solved solved the problem of recharging the battery.

The standard sensor suite has sensors for rain, air temperature, wind (speed and direction) and relative humidity. The temperature and humidity sensors are shielded from sunlight. The plus version of the sensor suite also has sensors that measure the solar radiation and the ultra-violet (UV) radiation. All HiSPARC weather stations are plus versions. The console of the system has its own sensors for the measurement of the internal local temperature and relative humidity and atmospheric pressure. Apart from these direct measurements the console calculates from these measurements the evapotranspiration rate, heat index, dew point and wind chill. More information about these variables is listed in section 4.1. In addition, the console displays time, moment of sunrise and sunset, moon phase and weather reports [54].

1In my opinion this should not always be true. Qualitatively we have:

P ·V

T =constant

therefore the volume only increases if the pressure stays constant

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Figure 4.2: The Vantage Pro 2 receiver, [55] and [54] (p.1).

Figure 4.3: The vantage pro 2 sensor suite on the rooftop of the NIKHEF (station 501).

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4.1 HiSPARC weather software

The HiSPARC weather station is controlled with a LabVIEW application. The basic structure of the software is outlined in the flowchart shown in figure 4.4. The software can be divided into four parts:

• Initialization

• Connect to weather station

• Select sensors

• Measure and send to database

Figure 4.4: Basic structure of the weather station software.

Initialization In the initialization phase the initial settings for data readout by the wheather station is retrieved from a file ‘weather.user.settings.ini’:

• COM port number

• Baud rate

• Start in data acquisition (daq) mode (True/False)

• Station ID

These settings are also displayed at the front panel (figure 4.5).

Connect to weather station In this phase the COM port specified in the initialization file is opened and the units for the weather variables are specified. We have chosen to use the factory chosen units (inch, degree Fahrenheit, mile per hour, mile). In the measure phase we convert them manually to more standard units (millimeter , degree Celsius, meter per second , and meter). In addition, the rain collector type is specified. Finally the dll version, weather station model number, software creation date and software version number are displayed on the front panel (figure 4.5).

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Figure 4.5: Screenshots of the front panel of the weather stations software with a tab that displays the weather station settings (above left), a tab that displays the error settings (above right), a tab that displays current weather data (below left) and a tab where weather data is graphed for the last hour and the last 24 hours.

Select sensors In this phase the sensors that are listed in the initialization file are displayed on the front panel. All available data is send to the database, the readings of which sensors the user decides to display on the front panel does matter. If the user changes the number of sensors it wants to display (on the settings tab figure 4.5) and presses

‘save’ this information is overwritten in the initial settings file.

Measure and send to database The measure phase is schematically described in figure 4.6. Weather data is read from the Vantage pro console, converted to standard units and the wind chill (using temperature and wind speed) and dew point (using temperature and relative humidity) are calculated. All data is displayed on the front panel (figure 4.5). Every once in a while, the difference ∆ between the computer time and GPS time (in seconds) is calculated by the HiSPARC DAQ software using the GPS antenna. This number is saved as plain text. We can use this difference to convert a computer weather time TP Cinto GPS time TGP S:

T_{GP S} = T_{P C}− ∆. (4.1)

All data is compacted in a weather data string that is send to the HiSPARC database.

Weather variables The fifteen weather variables that are stored in the HiSPARC database are listed in table 4.1.

Four variables (temperature inside, relative humidity inside and atmospheric pressure) are measured/registered by the Vantage Pro receiver that is located inside next to the HiSPARC computer. Seven variables (temperature outside, relative humidity outside, wind direction, wind speed, solar radiation, uv index and rain rate) are measured in the

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Figure 4.6: Flow chart of the measure phase in the weather station software.

‘sensor suite’ placed on the rooftop. Four variables (are measured by the sensor suite, evapotranspiration rate, heat index, dew point and wind chill) are derived from the other variables For more information about how to calculate the derived weather parameters you are referred to the Davis application note ‘Derived variables’ see [56].

If there is an error this is displayed on the error tab. There are four possible errors:

• COM port connection error (the serial port could not be opened)

• weather station data connection error (data could not be send from the console to the COMPUTER)

• database error (data could not be send to the local database)

• console time error (console time does not equal computer time)

For more information about software errors and how to resolve them you are referred to the installation manual included as appendix B.

Temperature Data logger We can measure the outside air temperature, but the temperature of the HiSPARC setup itself is also interesting. There is a temperature sensor attached to the PMT of plate 1 of detection station 502 (Science Park Amsterdam). Moreover another temperature sensor was placed at the bottom of the ski box of detector 1. The temperature sensor attached to the PMT was of the type EL-USB-TC-LCD from Lascar electronics. The sensor placed at the bottom of the ski box was of the type EL-USB1, also from Lascar electronics. Both sensors have an accuracy of ±1^◦C. For more information about the temperature probes see [42] (p.89-93), [58] and [59].

Search for a correlation between HiSPARC cosmic-ray data and

M ASTER ’ S THESIS P HYSICS

C

E

V

Search for a correlation between HiSPARC cosmic-ray data and

weather measurements

Author:

Loran de Vries Supervisor:

Prof. dr. Ing. Bob van Eijk, University of Twente Second reviewer:

Dr. Els. de Wolf, University of Amsterdam, Institute of Physics

National institute for subatomic physics, Amsterdam

August 7, 2012

Contents

Chapter 1

Introduction

Chapter 2

Cosmic rays

2.1 Appetizer: history of cosmic rays

2.2 Composition of primary cosmic rays

2.3 Energy spectrum

2.4 Sources of UHECRs

2.4.1 The Hillas plot

2.4.2 The GZK energy limit

2.5 Air shower development

2.5.1 Particle flux and composition at sea level

Chapter 3

Cosmic-ray detection

3.1 Energy loss in a scintillator

3.2 Photomultiplier

3.3 Electronics and trigger conditions

3.4 Shower data acquisition

3.4.1 Pulse heights

3.4.2 Event rate

3.5 Research question

Chapter 4

Atmospheric conditions

4.1 HiSPARC weather software

M ^ASTER ’ ^{S THESIS} P ^HYSICS