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Browsing by Subject "Monte-Carlo methods"

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    Observation of the Cosmic Ray Shadows of the Moon and the Sun Using the MINOS Far Detector
    (2019-12-13) Fogarty, Samuel J
    Cosmic rays are high energy particles, mostly protons and helium nuclei, that create muons on collision with the atmosphere. The Moon and the Sun block cosmic rays in their travels, so there should be less muons seen in the directions of the Moon and the Sun. These effects are known as the ‘cosmic ray shadows’. The purpose of this research was to find the cosmic ray shadows of the Moon and the Sun using muons observed by the MINOS Far Detector in the Soudan Mine in Soudan, MN. Muons deflect a considerable amount in the atmosphere and rock before they arrive at the detector, so there is a significant smearing effect in the shadows. This smearing effect makes resolving the shadows difficult, so a shadow template was created using Monte-Carlo methods. The template represents what the shadows should actually look like with muon deflection in mind. A background was created (using a Monte-Carlo method) to be the Null hypothesis that represents what the muon data would look like if the Moon and the Sun were not present. The shadow template, background, and signal histograms were compared using a Log-Likelihood Analysis to produce cosmic ray shadows of the Moon and the Sun seen by the Far Detector. The Moon and Sun shadows were produced using 141 million cosmic ray muons observed from October 2003 to September 2016. Confirming the expectation, the Moon shadow was shown to not vary much over time, whereas the Sun shadow varied considerably over time and was much less significant. The statistical nature of cosmic ray muon deflection was explored using binomial statistics and Monte-Carlo methods. It was shown that single and double cosmic ray muon deflection can be simulated using binomial statistics and Monte-Carlo methods.