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«STUDENT RESEARCH PAPERS SUMMER 2011 VOLUME 22 REU DIRECTOR UMESH GARG, PH.D. REU Student Research Papers – Summer 2011 University of Notre Dame – ...»

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Each of the four pairs of PWC planes contains one plane with 80 signal wires running east-west and the other plane has 80 signal wires running north-south for a total 160 wires in each chamber. The configuration of these two orthogonal planes is used to measure the angle of a charged secondary muon track to +/- 0.5°. All of the pairs of PWC planes, stacked one on top of the other, are separated by 200 mm of space and the bottommost two pairs are separated by a 50 mm thick steel plate that helps to differentiate between muons and electrons. When charged particles move through the detectors, several interactions can occur. If the charged particle is a muon, it will travel in a straight line directly through all of the PWC pairs. If the charged particle is an electron, it will either be completely stopped, start a shower, or scatters when it hits the steel plate. The plate is 96% accurate in distinguishing between muon and electron, but in view of the fact that the number of electrons seen at ground level is only a third of the number of muons that accuracy is closer to 99%.

The PWC chambers are filled with a gaseous mixture of 80% argon and 20% carbon dioxide. When a charged particle like a muon or an electron passes through a chamber, it will ionize the gas and a trail of ions will be left. These ionized particles will move through the chamber and as they do so will collide with other gas molecules. A current is formed on the closest signal wire that is amplified to denote the position of the wire in the chamber therefore the position of the particle.

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Project GRAND was established to do cosmic ray astrophysics, which investigates particles (or rays) that arrive at the earth’s surface from the sun and other galaxies.

When cosmic rays hit earth’s atmosphere, they collide with air nuclei, typically oxygen or nitrogen, and create secondary particles, such as pions. These pions quickly decay into muons that are able to reach earth’s surface to be identified by muon detectors. Project GRAND has the ability to detect rises in ground level muon flux that are associated with powerful solar flares as well as a Forbush decrease which is a rapid decrease in cosmic ray intensity followed by a slower recovery back to the normal muon flux. Forbush decreases are often caused by coronal mass ejections (CME), a massive burst of solar wind, light isotope plasma, and magnetic fields rising above the sun’s corona and being released into space. This ejected material consists of charged particles with magnetic fields. Traveling at an average of 500 km/s, CMEs will reach the earth in a matter of days and if the CME is strong, detectors will be able to see the Forbush decrease. The magnetic fields will disrupt the particles heading toward earth and deflect some of the cosmic rays from reaching the atmosphere causing a decrease in cosmic ray detection. Over just a few hours this decrease transpires, but it takes several days for the solar cosmic ray intensity to return to normal.

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There were several incidents of activity on the surface of the sun in the month of February, which may be an indication that the sun is coming out of a solar minimum in its normal eleven year solar cycle. The strongest of this activity was a X2 flare on February 15, 2011 with a peak at 1:56 UT [2]. Moving with a speed of about 900 km/s, the CME created by this flare was expected to reach earth’s atmosphere early on February 18, 2011. Several different detectors were examined to see if they saw any special phenomenon during this time period. The main detector that was inspected was the Oulu neutron monitor, which identifies secondary particles at a lower primary energy than Project GRAND. This means Oulu would have a better chance of seeing structure due to a Forbush decrease. The plotted data for February 12, 2011 00:00 UT to February 28, 2011 07:00 UT from Oulu can be seen in Figure

1. This neutron monitor saw definite structure in their neutron count with a 4% drop in a couple of hours and a gradual rise back to the normal count which is characteristic of Forbush decreases [3].

After seeing the structure in Oulu’s data, Project GRAND’s data was examined. The structure seen on GRAND’s website seemed to be more dramatic than Oulu. The data for GRAND can be seen in figure 2. GRAND’s data needed to be analyzed in more detail to make sure all non-physics effects have been properly taken into account.

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When the cosmic rays come into earth’s atmosphere, several factors can have an effect on them. The atmospheric pressure is one of the most influential factors on particles moving toward the surface of the earth. When the pressure is high, it takes particles more energy to reach the earth’s surface, meaning fewer muons reach the detectors. The opposite effect is true for lower pressure periods. A pressure correction that is completely accurate is essential if the physics of cosmic rays is to be studied. Without a proper pressure correction, the effects of the earth’s atmospheric conditions would overshadow the physics. The first check was to verify whether the GRAND’s data lab website was doing the pressure correction properly.

Several time periods were checked and the conclusion was that the website did not perform the pressure correction accurately. A new program had to be written to correctly handle the pressure correction and pressure corrections can now be made quite easily.

After applying the pressure correction, the data from the February period still showed more structure than that of Oulu so further investigation was necessary.

Additional corrections adjust for disabled stations or stations having problems with their counts. There is no simple way to check to see if the program that does this correction is working correctly, so another program was needed to verify this. This new program was written to take dayfiles from Project GRAND and find the stations with the highest counts for each ten-minute period that the experiment records, it would then combine the top four muon counts into a single count. This is not how

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data lab website. When a hut has a problem the efficiency goes down, meaning the muon count will decrease. By applying this top four station correction, it is possible to circumvent any problem that might be associated with the good hut correction. A pressure correction could then be applied to the count found by the top four station correction and data that was both pressure corrected and provisional correction for top stations would be available. After completing this step for GRAND’s February data, it was found that it was still resembled the original count data that seemed to have too much structure.

Other Possible Problems Although several steps were taken that would lead to a more accurate muon count, there is a great deal of structure still present in Project GRAND’s data and this leads to the notion that there might be more corrections or repairs that need to be done.

December 2010 through March 2011 was examined and there was found to be a definite correlation between average daily temperature and muon count. A section of this comparison can be seen in figure 3. At the end of December, there is a sharp increase in the temperature, which corresponds to a decrease in the counting rate.

This indicates that a proper temperature correction needs to be performed. This will be something that Project GRAND students will work on in the future. Another potential cause for the structure in GRAND’s data is gas leakage to the stations. This could affect the muon count rate if the gas flow wasn’t at a level it should be and the detector efficiency would be decreased. These are both potential problems that will be examined in order to verify that Project GRAND’s data is accurate.

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When looking at the activity seen by detectors during the solar active period in the middle of February 2011, specifically a suspected Forbush decrease on February 18, 2011, it was noted that data from Oulu Neutron Monitor in Finland and Project GRAND didn’t correspond with each other. The interesting observation that accompanied this was that Project GRAND, a higher energy experiment, saw a significant amount structure that was undetected by Oulu, which typically is able to see more activity due to its lower energy observations. Several methods of correcting the raw data were implemented to try and decipher if the data reported by GRAND’s data lab website was accurate and there is still an ongoing effort to establish this. It is necessary to exhaust every possible explanation for the structure seen by GRAND before the data can be viewed as significant cosmic ray activity.

Acknowledgements I would like to thank my advisor, Dr. John Poirier, for being very helpful and patient as I conducted by research. He made my REU experience very enjoyable as well as enlightening. I would also like to acknowledge the work of Calvin Swartzendruber, Chris Windbigler, and Evan Grimes with helping to maintain the detectors. I would like to thank Chris D’Andrea for his assistance when we struggled to find the next steps to take. Finally, I would like to thank Dr. Garg and the entire Notre Dame Physics Department for making this summer an exciting and educational experience.

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[1] J. Poirier, C. D’Andrea et al (2007), Status report on project GRAND, 30th International Cosmic Ray Conference (ICRC).

[2] NASA, http://www.nasa.gov/ [3] Oulu Neutron Monitor, http://cosmicrays.oulu.fi/ [4] NOAA National Weather Service, http://www.crh.noaa.gov/ Figure 1: This is the graphical representation of Oulu Neutron Monitor’s data. It shows a rapid decrease of about 4% in neutron count at the beginning of February 18 followed by a gradual increase back to normal [3].

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A link between the observed universe and simulated models of the universe can be made through the large scale structure characteristics of the galaxy redshift distribution. The output of simulated models can reveal a significant amount of information about the universe if the model can be verified and contrasted with observations of the actual universe. Computationally simulating the evolution of the universe results in information about the distribution of dark matter, baryonic gas, and galaxies in a finite volume with periodic boundary conditions. Using the galaxy distribution at different epochs. We distribute the galaxies in a larger space and compute the redshifts in order to create a model that reproduces surveys such as the Sloan Digital Sky Survey (SDSS), among others. The redshift calculation gives a measurement of the time elapsed since the light was emitted.

A computer program was created to manipulate the output from the simulated model’s distribution of galaxies. The program we use translates, rotates and shuffles the galaxy distribution then takes computed quantities (galaxy position and velocity) to calculate the redshift. The redshift distribution is then used to create a galaxy redshift catalog similar to the SDSS by imposing survey characteristics, such as magnitude limits in the different observed bands (or portions of the electromagnetic spectrum). This simulated galaxy redshift distribution program will be used to create a bridge between the simulated models of the universe and observations of the universe.

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Vesto Melvin Slipher at the Lowell Observartory noticed the velocities were not random, but were redshifted, indicating motion away from the Earth. From these observations Slipher continued on to find that 12 galaxies surveyed were moving away from the Earth and therefore redshifted, while the galaxy Andromeda showed a blueshifted spectrum meaning that it was moving towards the Earth. The observations of Slipher led to the conclusion that the galaxies were moving away not only from Earth, but one another as well through the expansion of the universe.

Over the period of 1914 to 1925 Slipher was able to measure velocities for 40 galaxies and confirmed that blueshift was much rarer than redshift, drawing the conclusion that most galaxies were retreating from Earth. [Carroll & Ostlie, 1996:1100-1112] In order to continue studying these motions of the galaxies many surveys of the sky have been done. These redshift surveys measure the apparent velocities of distant galaxies, which give astronomers an estimate of the distance away the galaxies are. From this distance astronomers are able to gain an idea of the levels of structure within the universe as illustrated in the image below taken from the Southern Sky Redshift Survey.

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surveys. Each dot represents a galaxy. (From L. N. Da Costa et al., A complete southern sky redshift survey, Astrophys.

J., 424:L1–L4, 1994) Considered one of the most ambitious and influential surveys taken in the history of astronomy, the Sloan Digital Sky Survey (SDSS) has obtained images and data of more than 930,000 galaxies and more than 120,000 quasars over eight years of operation. The SDSS uses a 2.5 meter telescope located at Apache Point Observatory in New Mexico and is equipped with a 120-megapixel camera imaging 1.5 square degrees of the sky at a time. 1 We use the data from a large scale hydrodynamical simulation, namely the positions of the galaxies and velocities, to calculate the redshift distribution. [Cen & Ostriker, 1999] We then apply limitations which the SDSS has in order to reproduce a redshift distribution similar to SDSS allowing us to make the connection between our simulated model and the observational universe of the SDSS images.

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and feedback processes used in the simulation are described in detail in Nagamin, Cen & Ostriker (2000). The chosen cosmology is the “concordance model” described by Wang et al. (2000). A data file containing the coordinates of each galaxy was provided and used to find the distance each galaxy is away from the point at which the galaxy distribution is viewed from. Within the program the galaxy distribution is able to be rotated, randomly shuffled and translated some distance away. The translation allows for the galaxy distribution to be viewed from various distances away, while the rotation of the axes allows a different view of the galaxy distribution.

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