April 2014 Volume 3, Issue 2
Correlation Between the Percent Cloud Cover and the Cosmic Ray Flux
S. Seth Roffé1*, Liza Baskin2, and John Valente2
Student1, Teacher2: Marine Academy of Science and Technology, Highlands, NJ
*Corresponding author: email@example.com
Cosmic rays are constantly bombarding the upper atmosphere, decaying into an array of particles in a shower called the hadronic shower. The very last particle that decays from a cosmic ray is the muon. Cosmic rays can be indirectly detected by using a detector that utilizes scintillator panels and photomultiplier tubes to detect the muons. The purpose of the experiment was to determine a correlation between the amount of cosmic rays detected and the percentage of cloud cover. The amount of cosmic rays detected was divided into five categories: one counter event, two counter events, three counter events, four counter events, and the total amount of muon triggers. Data was taken in Sandy Hook, New Jersey. There was a statistically significant (p < 0.01) positive correlation between one counter event, four counter events, the total amount of muon triggers, and percent cloud cover. There was a statistically significant (p < 0.05) positive correlation between two counter events and three counter events, and percent cloud cover. The findings of this study suggest that there is a relationship between cosmic rays and the climate and thus may affect climate change.
Cosmic rays are an array of atomic nuclei from the interstellar medium that are constantly bombarding the Earth’s atmosphere. The cosmic ray nuclei interact with particles in the Earth’s atmosphere causing a shower of subatomic particles. They are always interacting with the atmosphere, however, little is known about the effect they have on the Earth. The study of cosmic rays can potentially teach us much about how the Earth is affected by sources from space.
Cosmic rays take the form of high energy particles originating from far away in the galaxy. Most of the cosmic ray particles are protons, electrons, and atomic nuclei. Most nuclei are either hydrogen nuclei (protons) or alpha particles (helium nuclei). However, there are occasions where a cosmic ray is the nuclei of a heavier particle, such as lead 1. When these nuclei collide with particles in the upper atmosphere, they cause a hadronic shower. A proton decays into a positive, a neutral, and a negative pion. A pi meson, or pion, is an unstable particle that consists of a quark and an anti-quark and plays a role in the strong nuclear force. Charged pions immediately decay into muons and neutrinos. A muon is an elementary particle. This means that it is not known to have a substructure and is considered to be among the building blocks of all matter. A muon is vital in detecting the amount of cosmic rays striking the Earth’s atmosphere due to its charge, and relatively large lifetime. Cosmic rays cannot be detected directly. They, rather, are detected through the particles created as they decay, such as the muon 2.
A muon is similar to an electron, but it has a mass energy of about 105.6 MeV, about 200 times greater than that of an electron. The unit eV is a specific unit of energy used in quantum measurements, and it is equal to 1.602 x 10-19 Joules. A muon has the same spin and charge as an electron; however, it penetrates much farther than the electron 3. A muon is the final step in the hadronic shower of a cosmic ray. The muon only has a lifetime of about 2 microseconds (2.0 x 10-6 seconds) before it loses its energy, and decays 4. It travels close to the speed of light and therefore is affected by time dilation. If they didn’t follow time dilation laws of relativity, the muons would decay long before they would reach Earth’s surface and allow anyone to be able to measure them. This makes Einstein’s Theory of Special Relativity vital to detecting cosmic rays. Cosmic rays are indirectly detected by measuring the muons that the cosmic rays decay into.
There are concerns with cosmic rays in that they contain the potential to damage electronics such as satellites, due to the high amount of radiation they produce. The hadronic shower is thought to have an effect causing gamma radiation to be released from cumulonimbus clouds. Inside these thunderclouds, positive intra-cloud lightning forces the large-scale electric fields in the cloud to produce x-ray and gamma ray emission. The emission grows exponentially and produces very large energetic radiation. As the flux, or the rate of the transfer of particles per unit area, of electrons increases, the ionization causes the electric field to discharge with more radiation. This amount of radiation, x-ray and gamma, is very dangerous and may lead to health risks 5. The electrons that travel in these outbursts have energies above 30MeV which is some of the most energetic particles that are accelerated in the near-earth environment 6. Cosmic radiation is known to release gamma rays up to 800 GeV on average and recent observations of the High Energy Stereoscopic System, a system of atmospheric imagine telescopes, up to 1012 eV 7.
Cosmic radiation has a very high energy level and a very high charge. Exposure to this high energy radiation has been thought to increase the risk of developing radiation illnesses such as skin cancer. Studying cosmic rays and learning how to be protected from the radiation risks may lead to reducing the amount of radiation caused illnesses 3.
Radiative forcing is defined as the difference of radiant energy, or the power of electromagnetic radiation per unit area, absorbed by the earth and the energy radiated back into the interstellar medium. The radiative forcing was estimated to be at least five times greater due to solar irradiance changes. The total solar irradiance monitored from space for the last three decades reveals a well-established cycle of 0.08% 8. This may mean that cosmic radiation has an effect on global climate change. The radiative forcing has increased to +2.63 +- 0.26 W m-2. Studying cosmic rays could be informative about climate change as well as a way to reduce its effects. An increase in cloud cover is predicted due to the effects of climate change. This increase could have some relationship with an increase in cosmic radiation impact the Earth’s atmosphere 8.
Cosmic rays can be detected in the manner described, but its effect on the Earth is unknown as of now. Recently, it is thought that cosmic radiation and cloud cover have some correlation due to the fact that cosmic radiation is a dominant source of ionization in the upper atmosphere. It is thought that the cosmic ions may be the seeds that the microscopic water droplets form around, thus creating clouds 9. This would mean that there would need to be an abundant amount of particles passing the water molecules in the atmosphere to be able to clump them together to create a large mass such as a storm cloud. Therefore, there will be a direct correlation between the percentage of cloud cover and the amount of muons detected.
Materials and Methods
The materials used were a calibrated cosmic ray detector and a computer. The cosmic ray detector consists of four plastic scintillator panels with a photomultiplier tube for each scintillator panel. To prevent visible light from interfering with the experiment by striking the panels, each scintillator panel was heavily covered in black electric tape (Figure 1). This way, only muons were counted during the process of the experiment. The scintillators were then connected to a circuit board that was programmed to count and keep track of the muons using the scintillator panels (Figure 2). The computer software, RUcosmic from Rutgers University, utilizes different cosmic radiation programs and keeps track of the muons detected 10.
Due to possible variations in roofing material all experimental data was taken in the same location. The location where all data was gathered was the physics laboratory at the Marine Academy of Science and Technology at the Sandy Hook Unit of the Gateway National Recreation Area in New Jersey, USA (Figure 3). This building consisted of a single floor, with a subroof of acoustic tiles and a flat, tar papered roof.
Before taking data for each experiment, the detectors were calibrated using the protocol established by RU Cosmic, setting each panel to detect about 500 muons in 30 seconds with the “justcount” experiment 10.
To take data, cloud cover percentage was determined. The position used was stacked directly on top of each other for consistency. The “inclusive coincidence” experiment was used to keep track of the total muon triggers, one counter events, two counter events, three counter events, and four counter events. Times and dates to sample were chosen to keep an approximately equal number of cloudy and non-cloudy days. When sampling, the circuit board was connected to the computer via USB, and the rucosmic program was used. The detector was left running for any length of time. The detector was run over night for 24 hour long experiments occasionally; however, the length of time counted was at the liberty of the researcher. This process was repeated for each sampling event.
Once all of samplings were taken, each of the trigger and counter categories were divided by the amount of seconds counted in that experiment to get muons counted per second. This way, the data would be analyzed more accurately due to the variation in the amount of time counted. Once all of the data was in muons per second, the data was then statistically tested with the cloud cover percentage for a correlation.
Data was then recorded in excel and a Pearson’s correlation test was used to determine a correlation between each of the average muon trigger and counter categories and cloud cover. A Pearson’s correlation test was used due to the fact that a relationship between two parametric, such as mean muon triggers, continuous variables without a true independent variable was tested. The Pearson’s test results were then compared to a freedom chart based on the amount of samples taken to determine if the data was statistically significant.
The experiment was to determine if there is a correlation between the total amount of muon triggers and the percentage of cloud cover. There was a statistical significance (p < 0.05) between the results for two counter events, and three counter events and the percentage of cloud cover. There was a statistical significance (p < 0.01) between the results for one counter events, four counter events, and the total amount of triggers and the percentage of cloud cover.
The lowest amount of muons detected occurred in the four counter events during a percent cloud cover of 6% and 0% with a value of 8.0 muons per second. The highest amount of muons detected occurred in the one counter events during a percent cloud cover of 100% with a value of 78.4 muons per second (Table 1).
The lowest amount of total triggers occurred during a percent cloud cover of 6% with a value of 39.1 muons per second. The highest amount of total triggers occurred during a percent cloud cover of 100% with a value of 43.7 muons per second (Table 1).
There was a positive correlation between the one counter event (Figure 4), two counter events (Figure 4), three counter events (Figure 5), four counter events (Figure 5), and the percentage of cloud cover. There was also a positive correlation between the total amount of muon triggers and the percentage of cloud cover (Figure 6).
A Pearson’s correlation test was used individually for each of the trigger categories vs. percent cloud cover to get the correlation R-value of the data. The R-value of the one counter event data was 0.710. The R-value of the two counter event data was 0.572. The R-value of the three counter event data was 0.606. The R-value of the four counter event data was 0.683. The R-value for the total amount of triggers was 0.675. The R-values were then compared to a degrees of freedom chart to determine the p-value to see if the data was statistically significant. One counter events, four counter events and the total amount of muon triggers were statistically significant (p<0.01). Two counter events and three counter events were statistically significant (p<0.05).
The results obtained support the hypothesis and disprove the null hypothesis as there was a positive correlation between percent cloud cover and the number of muons detected. Additionally, one counter events, four counter events the total amount of triggers was considered statistically significant with a p value of less than 0.01. Two counter events and three counter events was considered statistically significant with a p-value of less than 0.05. Therefore, the null hypothesis that there will be no correlation between cloud cover and the amount of cosmic rays is disproven with a 5% and a 1% probably that the data obtained only happened by chance.
The data shows a direct correlation between cloud cover and the amount of cosmic rays, meaning that when there is an increase in cosmic radiation, there will be an increase in the amount of cloud cover. For the two counter events it was 0.572 (Figure 4). For the three counter events it was 0.606 (Figure 5). For the four counter events it was 0.683 (Figure 5). For the total amount of muons detected it was 0.675 (Figure 6). The strongest correlation occurred when detection of one counter events were used versus cloud cover with an r-value, or coefficient of correlation which shows the strength of the correlation, of 0.710 (Figure 4). It is thought that this happens due to the polarity of water molecules. As the charged particles from the cosmic rays pass the water molecules suspended in the air, the water molecules move towards the particle’s direction due to electromagnetic attraction. Since the muons have a negative charge, the positive hydrogen atoms in a water molecule will be attracted to the negative muon, so the entire water molecule will start to move towards the direction of the muon. As the water molecules move towards the same direction, they begin to clump together using their cohesive properties. Once enough water molecules clump together, a cloud is formed. The more muons that pass through the atmosphere, the more water molecules would feel and attractive force, and therefore, the more clouds will form. The data suggests that if there is a long term increase in cloud cover, it can be inferred that there would be an increase in detectable cosmic particles at the Earth’s surface.
The highest amount muons detected per second was on September 28, 2012 on one counter events with a value of 78.4 muons per second (Figure 4). As the hypothesis suggests, this highest value was detected with a 100% cloud cover. This value falls rather far from the average one counter events average of 74.9 ± 1.48 muons per second. The value is three standard deviations away from the average, showing that the value at 100% cloud cover is statistically different than other one counter events with a full spectrum of percentage of cloud covers.
The lowest amount of muons detected per second was on March 9, 2012 with a percent cloud cover of 6% and on October 22, 2012 with a percent cloud cover of 0%, with a value of 8.0 muons per second. The value falls below the average value for four counter events of 8.2 ± 0.12 muons per second, thus supporting the hypothesis that a lower percent cloud cover will result in a lower amount of muons detected. The lowest value falls two standard deviations away from the average amount of muons per second for four counter events (Table 1).
A direct correlation can be seen in each of the five categories with one counter events having the highest variation (Figure 4). This is due to the fact that one counter events have the most occurrences due to the fact that there is the highest probability of any of the scintillators being triggered at a moment.
Four counter events had the lowest variation to it with a standard deviation of 0.1 (Table 1). This would be due to the fact that there is the lowest probability that all four scintillator panels will be triggered by the same muon. The only way this occurs is when the specific muon has a substantially higher energy level than other muons. However, even with the lowest variation, there is still a positive relationship between percent cloud cover and muons detected (Figure 6).
The data above shows a pattern being that when there is a cloud cover percentage above 50%, there are more muons per second detected than the amount of muons per second detected for when there is a cloud cover percentage below 50%. This pattern remained true for all of the trigger and counter event categories. Therefore, it is suggested that with a higher percentage of cloud cover, there will be a larger amount of muons detected, showing a direct correlation between the two variables. It could then be predicted for future data, that when there is a high amount of cloud cover, a large number of muons will be detected.
Cosmic rays were thought to cause cloud formation, however, there was little statistical evidence that they were related. Because of this, there is much controversy over the effect cosmic rays have on the upper atmosphere. The data in this experiment shows statistical evidence of the relationship between the cosmic ray flux and the Earth’s atmosphere. There is statistical evidence that there is a direct correlation between the cosmic rays and the amount of clouds that form. There is no evidence that the hadronic shower formed from cosmic rays impacting the upper atmosphere cause the formation of clouds; however, there is evidence that there is a relationship between them. Cloud formation has many factors that affect it, and cosmic radiation shows evidence that it is one of those factors which may lead to an increase in understanding of global climate change and properties of the weather.
This data agrees with previous experiments on cosmic radiation. Pierce tested the ion-aerosol hypothesis and found that aerosol nucleation, the amount of condensation nuclei, is enhanced by the presence of ions in the upper atmosphere . Other results show that cosmic rays are a dominant source of ionization in the troposphere . If these results are true, then cosmic rays could indirectly relate to the amount of condensation nuclei that form in the upper atmosphere, which would agree with the positive correlation found in this experiment. All of the experiments together show that cosmic rays enhance ionization in the atmosphere, which, in turn, causes an increase in aerosol nucleation resulting in a positive correlation between the percentage of cloud cover that forms and the amount of cosmic rays that impact the upper atmosphere.
Sources of error may include seasonal variations in the cosmic ray flux, thus affecting the amount of muons detected, as well as cosmic activity interfering with the data collected, such as a solar flare. For future experimentation, it is suggested to take an even number of data throughout each season. Future data could be taken at different altitudes to determine if there is any variation in the results.
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