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Comparing Particle Detection Methods to Observe Atmospheric Interactions

Carlan IveyRockdale Magnet School for Science and Technology930 Rowland Road; Conyers, Ga

Introduction

Many interactions have been observed to occur between Cosmic Rays and Earth’s

atmosphere, some of which are crucial to important issues involving the state of the planet. With

cosmic rays being high energy primary particles, they are able to penetrate the atmosphere

starting from the outermost layers of the exosphere down past the troposphere directly to Earth’s

surface with extremely high energies. Though thousands of these particles are flowing through

human bodies every minute as muons, most of their effects occur within the atmospheric

environment. This phenomenon is due to higher energy Galactic Cosmic Rays (originating from

outside the galaxy with highest energies) and Solar Energetic Particles (originating from the Sun,

mostly accelerated during solar events) losing energy as they collide with nuclei gas and

molecules in the atmosphere. Physics studies around this ionization effect have produced results

involving how these particles could be a mechanism for climate change (J. Haigh; 2011), or even

whether it is the primary reason for facilitating the depletion of ozone (Q. Lu; 2009).

These atmospheric interactions caused by Galactic Cosmic Rays (GCRs) are more prominent due

to their much higher energy levels within GeV, yet Solar Energetic Particles (SEPs) are

becoming more effective due to the sun approaching its Peak of Solar Cycle 24, the solar

maximum period. During this period, increased amounts of Solar Events, such as Solar Flares,

Coronal Mass Ejections (CMEs), and Solar Winds many be notice more frequent and with higher

energies. Potential effects of increased temperature via climate change many be most effective

from cosmic rays.

However, by using physic technology these interactions may be studied more and potentially be

predicted for the exact effect with more evidence, possibly enough to avoid such events. A

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widely used program created by CERN, Geant4 Toolkit Setup, is used today to investigate

particles by computational simulation. Technologies used to directly measure particle showers

include particle detectors of various types (muon, neutron, neutrino, etc…), aerosol chambers

(ex. CLOUD chamber by CERN), or cosmic ray sensors on satellites (ex. SOHO). The possible

implications of particle technologies are numerous, bringing up a key question: Could

computational technology such as Geant4 be used to predict physical events of atmospheric

interactions?

Therefore, the purposes of this study into particle technology and cosmic ray impacts are 1) to

test the implication of atmospheric properties into Geant4 Simulations, 2) to predict the

interactions between atmospheric conditions and cosmic rays of galactic and solar origin, and 3)

to assess how conditions of the atmospheric environment are affected by cosmic rays that reach

Earth’s surface. By using physics technology on a computational, ground-level, and satellite

scale a large variation of information and predictions are expected to be found. However, the

hypotheses for the research are (Hypothesis #1) Using Geant4 Particle Simulations, the

interactions of Solar Energetic Particles and Galactic Cosmic Rays with atmospheric

components of ozone, clouds, and pressure can be predicted accurately. Specifically for

experimentation with Geant4, Sub-Hypothesis #1 states Muon particles in the simulation will

interact with more atmospheric conditions than protons, showing higher energy loss in the

column atmosphere. Finally, Hypothesis #2 states Cosmic Rays from Galactic origin will have a

larger effect on physical atmospheric properties, specifically those related to climate change due

to their higher energy levels, than that of cosmic rays from solar sources.

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Background Literature

Cosmic Rays are defined as high energy atomic particles that originate from outside

Earth’s atmosphere or the Solar System. These high energy radiation particles are capable of

holding energies of greater than 100 Giga Electron Volts (GeV), and with such immense

energies they can travel from light-years away to reach Earth. Once they reach Earth, the cosmic

rays can penetrate the atmosphere as their primary particles, such as protons and neutrons.

However, when they penetrate further into the atmosphere, they collide with nuclei gas and other

atmospheric molecules that cause the primary particles to decay into secondary particles in the

atmosphere. This includes secondary particles such as pions and kaons, yet other particles

produced that are primarily measured at ground level are particles called muons. Muons are

charged subatomic particles, or leptons, that exist numerously within Earth’s atmosphere and fall

towards sea level at about 1 muon/cm2/min (N. Ramesh; 2011). Most muon particles ionize

completely in the atmosphere, yet those that reach Earth are able to be measured for the

fluctuation energies of these particles which occur 24 hours a day.

The primary cosmic rays that penetrate Earth’s atmosphere are from more than one

source. While most cosmic rays originate from outside the Solar System produced from gamma

ray burst and supernovae remnants, other cosmic rays are produced from the sun. These high

energy proton particles, referred to mostly as Solar Energetic Particles (SEPs), have lower

energy levels than Galactic Cosmic Rays (GCRs), but are they produced closer to Earth and

directly effects Earth more. 80% of Cosmic Rays that reach the atmosphere are from galactic

sources, while the other 20% are from solar influence (J. Ryan:2010). These SEPs or Solar

Cosmic Rays are able to reach these energy levels in occurrences of strong Solar Energetic

Particle Events (SEP Events) on the sun, which consist mainly of Solar Flares, Coronal Mass

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Ejections (CMEs), and Solar Winds. Particles can be accelerated are higher densities and

energies, which can have noticeable effects on Earth.

SEPs are produced due to Solar Energetic Particle events, which cause a variety of

impacts on Earth. Solar Flares, the strongest SEP events, are classified on a scale from A to X

(A, B, C, M, X), and can cause direct effects on Earth. Flare Energies from their classes can be

converted to Watts/m2 using the following information:

A = < 10-7

B = 10-7 – 10-6

C = 10-6 – 10-5

M = 10-5 – 10-4

X = 10-4 – 10-3

The largest recorded effect of a solar flare in history, classified as Super X-class on a

scale of X to A class flares, caused terrestrial effects on Earth within days after its occurrence.

The event in 1859 was named the Carrington Event, where technological impacts occurred in

low level technology of the past, where telegrams were “sparking and glowing” due to the large

amount and energy SEPs that were penetrating Earth’s surface. Such an event today would

induce an immense effect on terrestrial life on Earth, causing failures in global electrical grids.

More crucial than technology, atmospheric impacts would consist of larger rates of global

climate change and ozone loss, most specifically at the poles, due to interactions of hazardous

gases and aerosols in the atmosphere with these particles.

Sunspots are formations of darker spots on the sun due to interferences in geomagnetic

activity on the surface. This occurs due to the formation of the magnetic field of the sun, which

burst through the photosphere, creating these sunspots (F. Nuevo: 2013). As the rotation of the

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sun continues at different points, the magnetic field becomes distorted, and burst at different

points causing releases in corona up through the sun’s atmosphere. These are solar flare

occurrences, which are high in density of electromagnetic radiation and energetic particles. Since

sunspot formations are described as disruptions in the photosphere due to distortion in the

magnetic field of the sun, it is able to be used to predict when solar flares occur. The magnetic

field is the main regulator SEP events and sunspots are visible indicators of it coming close to a

burst (J. Almeida: 2013). Yet, disadvantages in this method of observation are the inconsistency

in predicting certain properties. The method does not indicate what the specific energy levels of

flares, nor the time of the occurrence. The level SEP activity can be estimated by the number of

sunspots in different regions on the sun.

A recently discovered method of predicting solar flares is more efficient in determining

the factors of a solar flare occurrence. This method mainly involves observing changes in the

decay rates of radioactive isotopes in Earth’s atmosphere before flares (Fischbach & Jenkins:

2012). Radioactive Isotopes, or radionuclide, are atoms with unstable nuclei that can exhibit

energy that ionizes into new radiation particles. These particles occur naturally, and have

constant a half-life at which they decay. The half-life formula for radioactive isotope exponential

decay is expressed as N(t) N0e-ƛt , where N(t) is the remaining number of atoms after the

exponential decay after t years (P. Maurício: 2010). Radionuclides are known to have constant

decay rates over time, yet observations have negated the fact after changes in the decay of 226Ra

(Radium 226) due to a relation with the Sun-Earth distance and solar neutrinos that interacted

with the isotopes (P. Mauricio: 2010). However, the separate observation of 54Mn (Manganese

54) displayed that a change in the constant nuclear decay rate was apparent before the solar flare,

with a change over 24 hours before flare occurrence (Fischbach & Jenkins: 2012). The discovery

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again supported the fact that the decay rate variation is due to the Sun-Earth distance and

response to solar activity (flux of solar neutrinos). Evidence for the phenomenon was also with

36Cl (Chlorine 36) where annual variations were reported, and solar activity had an influence of

the length of the decay rates (Jenkins, Herminghuysen, Blue, Fischbach: 2012). This potential

system can be used to estimate specific time of Solar Flares, and impact by the Coronal Mass

Ejections (CMEs) they produced, being an advanced warning system.

The implication of advanced prediction systems would bring benefits for future

interactions with the SEP events. Currently the sun is in Solar Cycle 24, the 24th cycle since the

recording of sunspots began. This cycle began in 2008 with low solar activity, recorded as the

Solar Minimum period at which sun spot activity is low; the magnetic field is not as active, not

producing any major events. However, this period is followed by a Solar Maximum where sun

spot activity is higher, promoting more frequent and stronger SEP events (Richardson: 2013).

While strong solar flares have been unpattern during the cycle, the peak level of activity is

expected in late 2013 though earlier predictions thought it in early 2013 (Pesnell: 2008). During

the peak, X- classed flares are more likely to occur than any other time, presenting dangerous

effects on Earth regarding technology and the atmospheric conditions from increased SEPs.

The National Aeronautics and Space Administration (NASA) has predicted potential

technological effects from the Solar Maximum Period. This would consist of SEP influence of

satellites causing power failures, and disruptions in worldwide electrical grids. Possible effects

on atmospheric conditions can occur if SEPs with high enough energies penetrate the atmosphere

to reach atmospheric molecules. Galactic Cosmic Rays are known to have a large impact on

atmospheric conditions in the case of high particle rains from supernovae. They have impacted

Ozone loss at Earth’s poles after local gamma and supernova burst (Gehrels: 2003). The

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established correlation between cosmic rays and ozone pressure involves a long-term time

correlation, where ionization rates of cosmic rays with high energies effect Ozone loss at the

poles. Ionization causes the reduction of aerosols present in at the polar atmospheres, causing the

depletion of ozone molecules by CFCs (Q.B. Lu: 2009). The following interaction goes as Cl +

O3 => ClO + O2. Though it is a natural phenomenon, human contributions of green have made

the interaction hazardous for the atmospheric environment.

As far as climatology conditions on Earth, cosmic rays also play a role. Galactic Cosmic

Rays are known for causing low cloud amounts, which contributes to long term climate change

(Giles, Stephenson: 2006). However, solar activity greatly influences climatology patterns.

Anomalous weather patterns, mostly being increases in temperature, have been due to SEP

events, specifically solar flares producing CMEs (Haigh: 2011). Though GCRs have an inverse

relationship with Solar Activity due to Forbush Decrease, the output of sunspots are large

indicators of anomalous weather and climatology patterns (Lockwood: 2012). During the solar

maximum period, using prediction systems of observing sunspots and changes in radionuclide

decay rates could prove most efficient, yet observing climatology pattern could prove to be

helpful due to their direct involvements with sunspot activity and changes in the magnetic field

of the sun.

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Methodology

The method of experimentation for this project involves using different methods of

detecting cosmic rays, and investigating the accuracy of these particle technologies in using their

measurements of assess atmospheric interactions. In the experiment, the atmospheric interactions

of cosmic rays are observed by two different methods: computational simulation and physical

detection. The first method of cosmic ray measurements, which utilizes the CERN produced

Geant4 toolkit simulation, allowing accurate simulations of cosmic ray particles in the

atmosphere. The program is used in collaboration with Georgia State University, where

implemented conditions of ozone pressure, increased pressure, and cloud coverage were done.

The next primary method of measuring cosmic rays was using a cosmic ray muon detector. This

POT Muon Detector, is an instrument used to detect the passing of muon (-) particles through it

in defined intervals, in order to observe the energy and amount of primary cosmic rays reaching

Earth’s atmosphere. This detector was also used in collaboration with Georgia State University

Physics Department, where the detector remained in the labs during testing, taking measurements

in Atlanta, GA. The last method of observing cosmic rays, specifically from solar origin, was

using live measurements from NASA’s Solar and Heliospheric Observatory (SOHO) Satellite

(information is displayed on spaceweather.com).

The main procedures of the experiment are summarized in the following steps:

1) Set up the Geant4 Simulation GSU directory and implement the atmospheric conditions

into the program (Ozone Pressure, Cloud Coverage, Barometric Pressure).

2) Set up POT Muon Detector at Georgia State University Physics Labs to take specific

measurements (counts/hr).

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3) Run the Geant4 Simulation for 10 to 20 trials, varying the atmospheric conditions for the

trials. Analyze the simulation results from using protons and muons, using them to

predict what interactions may occur among physical cosmic rays from SEP and GCR

events.

4) Use online resources and archives from university (University of Alabama in Huntsville),

local (Peachtree Radiosonde station), and national (NOAA and NASA) stations to receive

measurements of atmospheric conditions implemented into the program. These

measurements will be upon the same daily/weekly trials for muon detection.

5) Compare physical measurements of cosmic rays to measurements of atmospheric

conditions to observe significant correlations. Using these results, conduct comparison

test to the Geant4 simulations.

Analysis methods consist of evaluating the data from particle technology on computational,

ground, and satellite levels. The Geant4 Simulation setup involves inputting commands to beam

particles through a column atmosphere, and results are displayed in Giga Electron Volt’s (GeV)

lost. Measurements from the POT Muon Detector are taken in counts per hour, which were

average for all 24 hours to get values to compare to the atmospheric conditions. For atmospheric

conditions, conditions of temperature, atmospheric pressure, and relative humidity were used

from archives of the Peachtree/University of Wyoming Radiosonde Station in Peachtree City,

GA. In addition, atmospheric ozone profiles were used from the University of Alabama in

Huntsville Ozonesonde Station VORTEX Database, received form faculty at the facility, for

ozone measurements. All measurements of atmospheric properties were taken in readings from

the stratosphere, as this is where most cosmic ray interactions with them occur.

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The most significant limitation in the experimentation in the conducted experiment is the

likelihood of extraneous events that cannot be accounted for in the Geant4 Simulation, therefore

not making final predictions completely reliable if there is an outlier due to a Solar Event, such

as a Coronal Mass Ejection or Solar Wind, or Galactic Event such as a nearby supernova.

The following is a photo of Carlan Ivey and Xiaohang Zhang, where he is teaching Carlan how to conduct Geant4 Simulations on a Macintosh Device (Location: GSU Physics Department).

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Photo 1

Data Analysis

In conducting the experiment, different analysis methods were used in order to analyze

the results from simulations and results from physical cosmic ray showers. Analysis methods

consist of evaluating the data from separate parts of experimentation to observe their variations

and significance, then comparing them together to analyze predictions. The Geant4 Simulation

results are analyzed within the program itself to determine energy loss values, therefore energy

loss quantities in the simulations will be compared among atmospheric conditions and particles

implemented into the program. For the physical detection of cosmic rays from Galactic and Solar

Events, analyzing consisted of taking measurements of all the atmospheric conditions

implemented into the simulation, and comparing them to particle data. Using Minitab Analysis

Software, statistics used consisted of Correlation Test and Linear Regression Analysis of

Variance (ANOVA) to observe relationships and delineate from “causation vs. correlation”. In

the final section of the data analysis, the significant values and interactions of both Geant4 and

Cosmic Ray results are compared to each other using a Covariance and Correlation Test to

determine relationship inferentially, and a prediction chart are used to describe the relationship.

Below shows the conditions set up in the Geant4 Simulation in order to run the trials. The

simulation variables of particle types, launch height, and particle energy were kept constant,

while atmospheric conditions and number of particles was varied. In efforts to keep the trial data

organized for each of the variables in the program, a chart was constructed. The chart list the

types of particles simulated along with each of the specific conditions and constants ran in. The

first column stating particle type was the dependent variables being compared, protons and

muons, for the Geant4 simulations. Atmospheric conditions were displayed in the second

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column, which are one of the independent variables used in the simulations. These conditions

consisted of regular atmospheric conditions of increasing pressure during a decrease in altitude

(Normal Conditions), High Atmospheric Pressure, where the most dense areas in the normal

simulation are constant throughout the column atmosphere, Ozone Pressure Increase, where a

noticeable layer of O3 is present in the simulation around the lower stratospheric altitude, and

Cloud Coverage, a variable that adds increased amounts of aerosol gas to the column atmosphere

to simulate clouds. Each of these conditions is qualitative in the chart, yet specific values for

density are within the simulation (kPa of atmospheric pressure, kPa of ozone pressure, ppmv of

aerosol molecules). The other independent variable of the Geant4 Simulations is the number of

particles launched; these were used to simulate occurrences of small particle rains rather than a

heavy particle shower after a SEP or GCR event. The other two conditions in the chart are

constants: Particle Launch Height was set at 50km in order to observe direct interaction with

stratospheric and tropospheric molecules, and Particle Energy, set at 100GeV to give an average

energy value to the particles to observe how much decrease in energy occurs. The described chart

is displayed in Figure 1.

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A total of 16 trials were run in the Geant4 Simulation to take measurements of all the different

conditions. These results were compiled into a table that outputs two main variable results: How

many particles reach the column surface and the average energy level of the particles that reach

the column surface. This chart can be observed for values for each trial conducted (Figure 2.).

Type of Particle: EN/Avg. EN of Particles (GeV): Number of Particles Reaching the SurfaceMuon(-) 11.719 2Protons 16 2Muon(-) 3.436 1Protons 6.362 0Muon(-) 8.627 1Protons 7.04 2Muon(-) 3 1

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Figure 1

Figure 2

Type of Particle:

Atmospheric Conditions:

Particle Launch Height (km):

Particle Energy (GeV):

Number of Particles:

Muon(-)None (Regular Pressure) 50 100 2

ProtonsNone (Regular Pressure) 50 100 2

Muon(-)High Atmospheric Pressure 50 100 2

ProtonsHigh Atmospheric Pressure 50 100 2

Muon(-) Ozone Pressure 50 100 2Protons Ozone Pressure 50 100 2Muon(-) Cloud Coverage 50 100 2Protons Cloud Coverage 50 100 2

Muon(-)None (Regular Pressure) 50 100 10

ProtonsNone (Regular Pressure) 50 100 10




Protons 5.0 1Muon(-) 16.32 10Protons 18.5 9Muon(-) 4.11 5Protons 6.39 3Muon(-) 13.38 8Protons 10.74 4Muon(-) 12.76 7Protons 12.83 8

The observations above are based directly off the computational output, where the Avg. Energy

of Particles were primarily used for the central tendency calculations while taking into account

the number of particles that reached the surface. More accurate representations of the results

chart above can be noticed in the following figures that display graphical representation. Each

chart was separately created based on the type of particles and whether or not they are in a single

event or high energy event.

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Figure 3

None (Regular Pressure)

High Atmospheric Pressure

Ozone Pressure Cloud Coverage 0

2

4

6

8

10

12

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Muon Energy Flux of Light Rains

Atmospheric Conditions of Simulation

Res

ultin

g A

vera

ge E

nerg

y L

evle

s (G

eV)

Figure 3 displays the Muon flux of the resulting energy when simulated in each condition for

single particle rains. It is observed that the highest energy is 11.719 GeV in Regular Pressure,

indicating the least amount of atmospheric interactions. The maximum interactions are in Cloud

Coverage (3.0 GeV).




2

4

6

8

10

12

14

16

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Muon Energy Flux of Heavy Showers


Res

ultin

g A

vera

ge E

nerg

y L

evel

s GeV

Figure 4 displays the Muon flux of the resulting energy when simulated in each condition for

heavy particle showers. It is observed that the highest energy is 16.32 GeV in Regular Pressure,

indicating the least amount of atmospheric interactions. The maximum interactions are in high

atmospheric pressure (4.11 GeV).

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Figure 4




101214161820

Proton Energy Flux of Heavy Showers


Res

ultin

g A

vger

age

Ene

rgy

Lev

els (

GeV

)

Figure 5 displays the Proton flux of the resulting energy when simulated in each condition for

heavy particle showers. It is observed that the highest energy is 18.5 GeV in Regular Pressure,

indicating the least amount of atmospheric interactions. The maximum interactions are in high

atmospheric pressure (6.39 GeV).

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Figure 6

Figure 5

Figure 6 displays the Proton flux of the resulting energy when simulated in each condition for

single particle rains. It is observed that the highest energy is 16.0 GeV in Regular Pressure,

indicating the least amount of atmospheric interactions. The maximum interactions are in cloud

coverage, which deviates from the rest of the charts (5.0 GeV).

This following section of analysis now extends upon the computer-based simulations of cosmic

rays, where physical conditions are analyzed for the specific dates for observing muon

interactions: 10/7/13 to 10/11/13, 10/14/13 to 10/18/13, and 10/21/13 to 10/25/13. In observing

cosmic rays directly from the sun (Solar Energetic Particles), the following dates used from the

major occurrences of M and X class solar flare are in the Figure 7:

Date Solar Flare ClassProton Density (Proton/cm2) Universal Time

Flare Energies Converted (Watt/m2)

10/24/2013 M9.3 1.0 0030 0.00009310/25/2013 X2.1 2.1 1504 0.0002110/28/2013 X1.0 2.0 0203 0.0001

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2

4

6

8

10

12

14

16

18

Proton Energy Flux of Light Rains


Res

ultin

g A

vera

ge E

nerg

y L

evel

s (G

eV)

Figure 7

10/29/2013 X2.3 7.6 2154 0.0002311/5/2013 X3.3 2.0 2212 0.00033

11/10/2013 X1.1 2.4 5140 0.0001111/19/2013 X1.0 4.1 1026 0.0001

The chart displays information about cosmic rays after a Solar Event (Flares). The values of

Solar Flare Class were converted to their Watts/m2, the actual measurements for corona particles

released from strong flares.

To analyze these measurements from the expansive values of atmospheric conditions collected

by radiosondes and ozonesondes, central tendency was calculated in order to find which values

would be most representative of the conditions at atmospheric altitudes. For Atmospheric

Temperature (Degrees K), Pressure (hPa), and Relative Humidity (%), the average of the

radiosondes measurements were taken, and for the ozonesonde measurements, the maximum of

Total Column Ozone (Dobson Units) and Ozone Pressure (mPa).

Pearson Correlation Values (r – value) were computed for all atmospheric comparisons to

cosmic rays in order to determine which interaction is significant enough to conduct Linear

Regression ANOVA on. For the Muon Values, the following chart displays the yielded Pearson

r’s used to determine correlation significance.

Correlation Variables Pearson r - value Avg. Daily Muon Flux(counts/hr), Avg. Atmospheric Pressure (hPa) 0.152 Avg. Daily Muon Flux(counts/hr), Avg. Atmospheric Temp. (K) -0.041Avg. Daily Muon Flux(counts/hr), Avg. Atm. Relative Humidity (%) 0.682Avg. Daily Muon Flux(counts/hr), Avg. Ozone Pressure (mPa) 0.574Avg. Daily Muon Flux(counts/hr), Total Column Ozone (DU) 0.553

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If the r – value is negative, the correlation is inverse instead of direct. If r > 0.5 then the

correlation is at least 50%, and if r = 1, the correlation is 100%. The highlighted values are those

used for Linear Regression Analysis due to their correlations being above 50%.

Using the cosmic rays detected by the POT Muon Detector accounts for cosmic rays from

galactic sources. The first Linear Regression Analysis below (Figure 8) consists of Avg. Daily

Muon Counts vs. Relative Humidity:

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Figure 8

Correlation Variables Pearson r - valueProton Density (protons/cm^3), Avg. Ozone Pressure (mPa) 0.513 Flare Energy Class (Watts/m^2), Avg. Atmospheric Pressure (hPa) 0.281 Flare Energy Class (Watts/m^2), Avg. Atm. Relative Humidity (%) -0.401Flare Energy Class (Watts/m^2), Avg. Atmospheric Temp. (K) 0.615Flare Energy Class (Watts/m^2), Avg. Atmospheric DewPoint (C) -0.052Flare Energy Class (Watts/m^2), Avg. Ozone Pressure (mPa) 0.362Flare Energy Class (Watts/m^2), Total Column Ozone (DU) 0.519

Relative Humidity, the density of water vapor air is holding, is representative of Cloud Coverage

in the atmosphere. The r – value is 0.682, indicating a higher correlation possible, where the

regression equation is: Atmospheric Relative Humidity (%) = - 857 + 0.0285 [Avg. Daily Muon

Flux (counts/hr)]. The p – value on 95% confidence interval is P = 0.007 and the F = 10.44, a

significant value in indicating a direct interaction between Cloud Particles and Muon Particles.

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The Ozone Pressure, the density ozone molecules at a given altitude, was taken at the maximum

altitude to ensure interactions can be observed. With r = 0.574, the regression equation is Ozone

Pressure (mPa) = - 119 + 0.00425[Avg. Daily Muon Flux (counts/hr)]. P = 0.032 and F = 5.88,

indicating another significant value that proves an interaction with muon particles.

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Figure 9

Figure 10

The difference between Column Ozone and Ozone Pressure is that ozone pressure is the density

of ozone from a specific altitude down to the surface of Earth, therefore its measure in Dobson

Units. R = 0.553, and the regression equation is Total Column Ozone (DU) = - 1873 +

0.0663[Avg. Daily Muon Flux(counts/hr)]. The F = 5.30 and P = 0.040, giving a significant

value that indicates another interaction with this measure of ozone at a maximum altitude.

The following scatterplots move on to the analysis of atmospheric conditions with data collected

from NASA’s Solar and Heliospheric Observatory on Solar Energetic Particles. Two measures

of these cosmic rays were used, Proton Density (proton/cm3) and Flare Energy (Watts/m2), yet

more correlation was found in Flare Energy.

Proton Density had a larger r – value than its p – value only when compared to Ozone Pressure.

With r = 0.513, the regression equation is Ozone Pressure (mPa) = 12.8 + 0.239[Proton Density

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Figure 11

(protons/cm^3)]. With F = 1.79 and P = 0.239, which does not indicate a complete atmospheric

interaction between Proton Density and Ozone Pressure.

Average Atmospheric Temperature varies as a radiosonde ascends into the atmosphere, therefore

it is averaged, and converted to Degrees Kelvin so that no negative values are present. R = 0.615

and the regression equation is Avg. Atmospheric Temp. (K) = 233 + 13077[Flare Energy Class

(Watts/m^2)]. With F = 3.04 and P = 0.142, the interaction, or in this case causation for Flare

Energy on Atmospheric Temperature, is almost significant.

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Figure 12

Total Column Ozone compared to Flare Energy is another measurement for ozone in a different

perspective than Ozone Pressure, yet it should yield similar results. R = 0.519 and the regression

equation is Total Column Ozone (DU) = 197 + 199893[Flare Energy Class (Watts/m^2)]. Since

F = 1.84 and P = 0.233, the interaction cannot be concluded to occur, as it is for Ozone Pressure.

From the analysis of cosmic rays of galactic and solar sources to the atmospheric conditions,

there are only six interactions that are physically more significant that the others, indicating some

interaction, though it might not be a direct correlation. The interactions are likely to occur in

stratosphere where most of these properties are at their largest effect due to the ionization

mechanism of cosmic ray particles. From the Geant4 Simulations, specific interactions are

implied to occur in the atmosphere, resulting in cosmic ray energy loss. To observe if these

reactions in the column ozone of the program can be predictive of physical occurrences, a

Covariance and Correlation Test were conduct using specific physical cosmic ray values that

represent interactions (Figure 14).

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Figure 13

Geant4 Proton Energy Loss

Geant4 Muon Energy Loss

Flare Energy (Watt/m^2)

Muon Flux (counts/hr)

18.5 16.32 0.000093 309236.39 4.11 0.00033 3156210.74 13.38 0.00011 3111012.83 12.76 0.0001 31131

Covariance is used to determine if they is a common variation in the compared variables. If the

value is negative and ≠ 0, then the covariance is inverse, and if the value is positive and ≠ 0,

covariance is direct.

When Geant4 Proton Energy Loss and Flare Energy (Watt/m2) are compared, covariance =

-0.000458 and r = -0.794, showing that the variables change at a very low value and the

relationship is inverse. Though correlation value is high (almost -1), P = 0.206. When Geant4

Muon Energy Loss is compared to Muon Flux (count/hr), covariance = -1418.638 and r = -0.998,

indicating the variables change at a considerably high rate and the correlation is almost directly

inverse. With P = 0.002, the predictions are almost completely accurate.

Geant4 Muon Simulations Energy Loss

Geant4 Proton Simulations Energy

Loss

Flare Energy Particles

(Watts/m^2)Muon Flux (counts/hr)

Atmospheric Pressure Interaction Occurs Interaction Occurs Slight Interaction XAtmospheric Temperature X X Interaction Occurs XRelative Humidity (Cloud Coverage)

Significant Interaction Interaction Occurs X

Significant Interaction

Ozone Pressure Slight Interaction Significant Interaction Slight Interaction


Total Column Ozone Slight Interaction Significant Interaction Interaction Occurs


Finally, a prediction chart (Figure 15) was created to descriptively analyze what interactions are

occurring.

Results and Conclusion

25

Figure 14

In analyzing the parts of the experimentation, the three main sections involved

investigating Geant4 Cosmic Ray Simulation outputs, physical Muon and Proton measurements

compared to atmospheric conditions, and if Geant4 results could predict results of particle

technology detections. The particle technology that emphasis is placed on is the use of

computation, ground-level detection, and data from satellite readings for the independent

variables, as well as using resources that use radiosonde and ozonesonde technology to take

measurements in the atmosphere.

First, the analysis results of the Geant4 Simulations were conducted by testing variables in the

program, primarily types of particle and their energy loss due to interactions with atmospheric

conditions implemented: Cloud Coverage, Barometric Pressure, and Ozone Pressure. Losses in

energy from the particles in the simulation indicate interactions with atmospheric properties,

where it is predicted muon particles in the simulation will interact with more atmospheric

conditions than protons.

Under the conditions of normal atmospheric pressure, this was the control behavior for the

protons and muons in the column atmosphere (100km x 28.5km). The change in energy was

normal and the same number of energy particles reached the surface, which indicates low

amount of atmospheric interactions (particle values: muon- 11.719GeV/2 particles and proton-

16.00GeV/2 particles). Under conditions of higher atmospheric pressure, a considerable decrease

was noticed in both the energy of particles after the launch and in the number of particles that

reached the surface in the showers (shower values: muon- 4.11GeV/5 particles and protons-

6.39GeV/3 particles). Under conditions of ozone pressure , the energies for the particles that

reached the surface for protons was lower than that of muon energy, in which this is the first

case. This indicates more interaction from the protons than the muon particles and showers

26

(shower values: muon- 13.38GeV/8 particles and protons- 10.47GeV/4 particles). Under

conditions of cloud coverage, the values were considerably close to each other, indicating a

similar atmospheric interaction between both proton and muon showers and particles (shower

values: muon- 12.76GeV/7 particles and proton- 12.83GeV/8 particles).

When compared together, the mean value of for muon showers is Xmuon GeV = 11.6425 and the

mean value for proton showers is Xproton GeV = 12.115. Based off the analysis in the Geant4

program, the sub-hypothesis is supported, where muon particles have more interactions with

atmospheric properties due to higher energy loss. These results were used again in observing

accuracy of predictions.

Analysis of physical muon and protons particles using the independent variables of POT Muon

Detector and NASA’s Solar and Heliospheric Observatory satellite resource for detection

compared to the same conditions implemented into the simulation to observe live interactions.

Based on correlation values for Muon Fluctuations, conditions of Atmospheric Relative

Humidity, Ozone Pressure, and Total Column Ozone had values r > 0.5. When Linear

Regression ANOVA was conducted on the three data sets, it was found that each was statistically

significant (Relative Humidity P= 0.007, Ozone Pressure P= 0.032, Total Column Ozone P=

0.040). This indicates a direct interaction between Cloud Molecules and Ozone pressure from

galactic cosmic rays.

Based on correlation values for Proton Density and Flare Energy, conditions of Average

Atmospheric Temperature, Ozone Pressure, and Total Column Ozone had correlation values r >

0.5. After using Linear Regression ANOVA testing, the correlations were found to be high, yet

the p – values do not indicate a major significance (Flare Energy vs. Atm. Temperature P= 0.142,

27

Proton Density vs. Ozone Pressure P= 0.142, Flare Energy vs. Total Column Ozone P= 0.233).

Therefore a major interaction between these properties is not a great as interactions from Muon

Flux.

The last section of analysis consisted of observing if the results from the Geant4 Simulations

could be used to predict the physical occurrences of cosmic ray interactions. For measurements

or Muon particles, statistics report r = -0.998 and p = 0.002, indication almost a complete

correlation between the simulation and physical measurements due to significant values. In

measurements of Proton particles, statistics state that r = -0.794 and p = 0.206, where the

relationship is very strong but predictions are not accurate enough.

After analysis of the three sections of the experimentation, conclusions to hypothesis are:

Hypothesis #1) Using Geant4 Particle Simulations, the interactions of Solar Energetic Particles

and Galactic Cosmic Rays with atmospheric components of ozone, clouds, and pressure can be

predicted accurately, is not rejected, where Galactic Cosmic Rays had more accurate predictions

to Muon Flux. Sub-Hypothesis #1) Muon particles in the simulation will interact with more

atmospheric conditions than protons, showing higher energy loss in the column atmosphere, is

supported due to the output energy loss being greater for muons than protons. Hypothesis #2)

Cosmic Rays from Galactic origin will have a larger effect on physical atmospheric properties,

specifically those related to climate change due to their higher energy levels, than that of cosmic

rays from solar sources, is not rejected since interactions with Relative Humidity, Ozone

Pressure, and Column Ozone have significant interactions with Muon Fluctuations.

Since the experimentation was on a computational level that uses a Linux Environment to sum

up the functions, limitations of this research are not as wide since it is not completely off

28

physical measurements. Yet, there is a limitation for things that cannot be accounted for in the

simulation, such as an extraneous event that could occur during the trials. These cannot be

account for in the simulation even though it randomly distributes particles in the program. Still,

the reason for taking physical measurements was to investigate if such events could occur, which

would alter predictions made.

The implications of the research conducted have tremendous value, specifically concerning the

impact that cosmic rays will have in the future on our atmosphere and terrestrial life. These

implications can be applied to each part of the experiment for each purpose, explaining what

potential investigations can be conducted in the future involving cosmic rays. From a standpoint

of particle technology, the implication of atmospheric conditions into a 100km x 28.5km

atmosphere column can be efficiently used to predict how Earth’s atmosphere will react, and

what to expect on Ground-Level muon detectors. The Geant4® Toolkit created by CERN has

tremendous applications to particle technology. However, the more notable implications into the

atmospheric environment can apply by this research. The assumption that muon particles would

have more interactions was based on knowledge of higher energies of Galactic Cosmic Rays

compared to Solar Energetic Particles. Yet, as the sun proceeds through its Solar Maximum of

Cycle 24, Solar Energetic Particles can increase in energy having more atmospheric effects,

especially after higher class flares than those observed.

Therefore, the mechanisms of ionization oxidation from cosmic rays of high energies are

occurrences that will continue due to presences of anthropogenic gases in the atmosphere and

galactic cosmic rays. The atmospheric properties most susceptible to this mechanism are cloud

coverage and ozone pressure, the two factors that were affected most by muon flux. With

presences of Chlorofluorocarbons (CFCs), Carbon Dioxide (CO2), and Sulfur Dioxide (SO2) still

29

in the atmosphere, they are activated by the ionization mechanism to cause low cloud amount

and ozone depletion (at poles). These results can have valuable input in a challenging debate

about climate change: Is climate change an induced event due to human contributions of

anthropogenic gases and aerosols, or is it a natural event due cosmic ray ionization? From the

results of this project, it is arguably both, where humans have worsened the effect of natural

climate change.

Further research into particle technology would be to use different methods of technology to

observe properties of cosmic rays, such as experiments conducted by CERN in the CLOUD

Experiment (CERN Collaboration: 2010). Constructing an aerosol chamber would be on method

on getting a visual of cosmic rays as they pass through the chamber. Yet, Geant4 can still be used

for more applications than beaming particles through a column atmosphere, where properties of

particles can be observed computationally. Another endeavor in particle technology would be to

construct a mobilized detector that could detect particles over a range within an area, where

further observations of cosmic ray interactions with atmospheric conditions can be found, and

possibly be used to work towards establishing mechanisms that occurred in this research.

Acknowledgments

30

For the performed experiment to have taken place, many professionals and graduates must be

acknowledged for contributions toward the project.

In the first part of experimentation, Geant4 Simulation Analysis of cosmic ray particles was

conducted. Acknowledgments are given to the organization that designed the toolkit that allows

for different physics departments and research facilities to run simulations, CERN. The

simulation that was created in a Linux environment was designed by team of Dr. Xioachun He,

Mathes Dayandanda, and Xiaohang Zhang of the GSU Physics Department. Special

acknowledgments are given to the graduate student Xiaohang Zhang for assisting in setting up

the directory to the GSU Server on the Macintosh device used to run the Geant4 Simulations. In

addition, acknowledgments are given to the team for assisting in implementing the required

atmospheric conditions for the experimentation.

For the second part of the experimentation, the modified muon detector was constructed at

Georgia State University Physics Labs. In guiding with using the POT Muon Detector

acknowledgements are given to Dr. He, Xiaohang Zhang, and Professor Carola Butler. The

detector was set up at the physics labs, and the location of measurement was in Atlanta, GA.

Acknowledgements are also given to Mathes Dayandanda helping me in using online resources

that Georgia State University Physics Department has connections with. In addition,

acknowledgements are given to the team at University of Alabama in Huntsville for sending me

the VORTEX Database of ozonesonde launches.

Acknowledgements are given to my research teacher Mr. Hendrix for assisting in data analysis

and guiding progress of the project.

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31

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34

Appendences

A) Experimental Design Diagram

IV: Particle Detection Method / Proton & Muon Particles IV Levels: Geant4 Particle

Simulations (Representative of GCR

and SEP Events)

Solar and Heliospheric Observatory Satellite

Measurements on Solar Events

Stationary Muon Detector (POT

Scintillator Liquid)

Trials: (Days)

10 - 20 10 - 20 10 - 20

DV: Cosmic Ray Response from Atmospheric Conditions: Temperature, Barometric Pressure, Cloud/Aerosol Molecules, Ozone Pressure

Constants: Type of Detectors, Location (GSU & Home), Type of Particles Observed (Protons & Muons)

B) Materials and Instruments

The following are main instruments and programs used during the experimentation to take

measurements of the variables:

Geant4 Toolkit Simulation Setup (Linux environment created by Professors and

Graduates at GSU.

Cosmic Ray-Muon Detector (used with GSU; POT Detector )

The following are a list of other important materials involved in 1) running the Geant4 Programs

and 2) taking measurements on physical conditions and cosmic rays:

MacBook Pro PC

- Macintosh Terminal Program

- GSU Terminal Directory

Geant4 Linux Setup

Muon Detector

35

- Liquid Scintillator

- Photomultiplier tube

- Personal Computer

- QuarkNet software

Microsoft Excel

Minitab Analysis Software

4GB Storage Drive

The following are the online archive resources used in receiving data on the various atmospheric

conditions:

Solar and Heliospheric Observatory (SOHO) Satellite (www.spaceweather.com)

University of Alabama in Huntsville Ozonesonde Station – Vortex Database (sent to

student by faculty of the station)

Peachtree City Radiosonde Station (run by University of Wyoming)

C) Detailed Procedures

The experiment being conducted requires some qualified scientists that were worked with from

different universities (ISEF Requires a Qualified Scientist Form). The data collection of the

experiment is being conducted at the university facilities (ISEF Requires Research Facility

Form).

Safety Precautions

In conducting the project, safety precautions were required in using particle detectors.

Professional assistance was required from mentors and graduates at Georgia State University.

This required using materials to construct and calibrate the mobile scintillation unit for the

modified detector. The detector was used at the GSU location to ensure safety of use.

36

Procedures – Geant4

Below gives a description of procedures used in conducting the first part of the project, Geant4

Simulation Analysis. This is where simulation results in varied atmospheric conditions are

compared to data collected from ground level detectors. The simulation directory and ground

level muon detector were used in collaboration with Georgia State University.

1) A Macintosh PC device must be used to conduct the simulations. Open the Terminal on

the desktop. Log into the Georgia State University (GSU) Geant4 server on a Macintosh

PC.

- The Geant4 simulations can also be conducted on Windows PC, which requires

installation of the Cygwin interface similar to that of Macintosh Terminal.

2) The program must be set up using the C++ commands since the simulation interface is in

a Linux environment. Use the given login account (Username: phy3300-st22) with

password to access the GSU Server.

3) (Only Needs to Be Performed Once) Insert the specific commands in order to set up

the work directory from the GSU Server to the Macintosh device. This will link the

needed variables for the simulation of the device so that simulations can be performed

effectively.

4) Set up the Running Environment by entering “tcsh” command to access the environment

shell and enter a “source” command so the simulation variables can be accessed.

37

5) Once the Running Environment is set up, use the “./MuELoss” command in order to

create the display window that provides the Geant4 Simulation (Can only be done if you

are already in the “g4work/cosmic” directory).

6) Vary the atmospheric conditions to compare to atmospheric conditions. These includes:

- Atmospheric Density (mPa)

- Ozone Density (mPa)

- Cloud Coverage

Launched at a constant height of 30km – 40km to interact with the Stratosphere

components.

7) Use the “/gun/particle (Type of Particle)” command to adjust the type of particle that is

being simulated. The particles observed for atmospheric interactions were protons

(“proton”) and negatively charged muons (“mu-“).

8) Use the “/run/beamOn (Number of Particles)” command to adjust the number of particles

that fall through that atmospheric column in the simulation (used to simulate a single

particle or a large-scale GCR or SEP event).

9) Use the “/gun/energy (Energy Level of Particles) GeV” command to assign the energy

level to the particles in the simulation. The energy is in Giga Electron Volts (used to

assign proper energy levels between secondary muons particles and primary proton

particles).

10) Run the simulation and record the results:

- Record the results display the energy loss of the particles, which are compared to the

data of muons counts from Earth’s atmosphere and their energy levels.

38

Record the conditions that the particles were simulated in, including atmospheric changes to the

program.

39

40

Go to GSU to receive Geant4 Simulation setup for varied atmospheric

conditions (cloud coverage, ozone layers,

atmosphere density).

Repeat muon flux data collection for 10 – 20 trials. Simultaneously, use archive

resources to get data on atmospheric conditions

occurring.

Repeat Geant4 Simulation procedures for 5 – 10 trials. Keep constant some variables (height & energy particle) while varying others (atmosphere conditions, number of

particles).

Experimental Flow Chart

Obtain instruments and necessities for the POT

Muon Detector and Geant4 Simulations (Collaboration

with GSU).

Record the results of the simulations in terms of energy loss (particle decay) for each trial. Also record conditions it

was run it.

Was there a significant atmospheric interaction between any cosmic rays?

Experimentation Part 2: Go to Georgia State University

Physics Labs to set up muon detector to take measurements in counts/hr (POT Detector).

Experimentation Part 1: Run Geant4 Simulation for trial 1. 1) Observe proton particles to

simulate SEP events. 2) Observe muon particles (-) to

simulate GCR secondary particles and normal muon flux.

Experimentation Part 2: Use SOHO satellite data to observe for major solar weather events

for a period of a month.

Compare cosmic ray measurements from

galactic/solar sources to atmospheric conditions

measured in order to find significant interactions.

Use simulation results to compare to physical

measurements of cosmic rays in order to observe efficiency of Geant4 at making accurate

predations Was the Geant4 Simulation accurate at

making predictions?

D) Raw Data

The chart below displays the settings for the Geant4 Simulation trials. It includes the conditions,

variables, and constants for which the Geant4 Test and Analysis was conducted in order to make

predictions based on the results of these particle rains.

Type of Particle:

Atmospheric Conditions:

Particle Launch Height (km):

Particle Energy (GeV):

Number of Particles:

Muon(-) None (Regular Pressure) 50 100 2Protons None (Regular Pressure) 50 100 2



Muon(-) Ozone Pressure 50 100 2Protons Ozone Pressure 50 100 2Muon(-) Cloud Coverage 50 100 2Protons Cloud Coverage 50 100 2Muon(-) None (Regular Pressure) 50 100 10Protons None (Regular Pressure) 50 100 10




41

The following images below are physical appearances of the Geant4 Simulations on the

Macintosh device used to run the simulation. Below shows different trials for the simulation:

42

The following data profiles are the central tendency of atmospheric measurements used for

analysis of muon fluctuations:

00Z

10/7/2013 Pressure (hPa) Temperatue (K) Dewpoint (C ) Relative Humidity (%)Average: 385.3 Average: 240.4 Average: -37.1 Average: 62.3






43







10/23/2013 Pressure (hPa) Temperatue (K) Dewpoint (C ) Relative Humidity (%)Average: Average Average Average

10/24/2013 Pressure (hPa) Temperatue (K) Dewpoint (C ) Relative Humidity (%)

44

Average: 293.1 Average: 235.4 Average: -53.58 Average: 18.66


12Z







45




10/18/2013 Pressure (hPa) Temperatue (K) Dewpoint (C ) Relative Humidity (%)Average: 345.2 Average: 240.8 Average: -45.10 Average: 35..61



10/23/2013 Pressure (hPa) Temperatue (K) Dewpoint (C ) Relative Humidity (%)Average: Average: Average: Average:

46



The following is the same data for dates of solar events:

00Z



10/28/2013 Pressure (hPa) Temperatue (K) Dewpoint (C ) Relative Humidity (%)Average: Average: Average: Average:


47




12Z





48




The following is a central tendency chart used to compare ozone measurements of cosmic rays

Week Avg. Atm. Ozone Value (mPa) Total Column Ozone (DU)10-7 to 10-11 14.16 220.9810-14 to 10-18 13.7 196.3610-21 to 10-25 11.54 176.8210-28 to 11-1 14.85 253.4311-4 to 11-8 13.59 275.5311-11 to 11-15 13.04 257.7611-18 to 11-22 13.68 231.28

49

The following is a chart of the solar weather measurements used:

Date Solar Flare Class Proton/cm^3 Universal Time Flare Energies Converted10/24/2013 M9.3 1.0 30 0.00009310/25/2013 X2.1 2.1 1504 0.0002110/28/2013 X1.0 2.0 203 0.000110/29/2013 X2.3 7.6 2154 0.00023

11/5/2013 X3.3 2.0 2212 0.0003311/10/2013 X1.1 2.4 5140 0.0001111/19/2013 X1.0 4.1 1026 0.0001

50

The following are muon fluctuations for counts per hour, with the average values that were used

displayed below:

mm/dd/yyyy hr counts/hr10/7/2013 0 3167410/7/2013 1 3164110/7/2013 2 3181510/7/2013 3 3172510/7/2013 4 3141710/7/2013 5 3166710/7/2013 6 3163710/7/2013 7 3147310/7/2013 8 3148510/7/2013 9 3135410/7/2013 10 3166910/7/2013 11 3169010/7/2013 12 3145610/7/2013 13 3172510/7/2013 14 3149310/7/2013 15 3141010/7/2013 16 3163910/7/2013 17 3138510/7/2013 18 3172410/7/2013 19 3138110/7/2013 20 3157810/7/2013 21 3153710/7/2013 22 3146410/7/2013 23 31451

Average 31562

mm/dd/yyyy hr

counts/hr

10/8/2013 0 3131810/8/2013 1 3138610/8/2013 2 3161810/8/2013 3 3146110/8/2013 4 3156510/8/2013 5 3155910/8/2013 6 3138610/8/2013 7 3151710/8/2013 8 3118910/8/2013 9 3133010/8/2013 10 3152110/8/2013 11 3153510/8/2013 12 3125510/8/2013 13 3135110/8/2013 14 3160010/8/2013 15 3135110/8/2013 16 3127510/8/2013 17 3144410/8/2013 18 3127610/8/2013 19 3130510/8/2013 20 3125210/8/2013 21 3117810/8/2013 22 3127810/8/2013 23 31014

Average 31373.5

51


Average

31154.5833


Average 31275.5

52


Average

31335.7917


Average 31182.25

53


Average 31217.91667


Average 31270.79167

54


Average31357.9583

3


Average

31350.45833

55


Average 31131.375


Average 31348.25

56


Average

31461.58333


Average 31109.54167

57


Average 30922.625

58

comparing particle detection methods to observe ... · web viewwith cosmic rays being high energy...

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