Bellido-Caceres, JoseDawson, BruceAhumada-Becerra, Jason Alexander2024-07-252024-07-252024https://hdl.handle.net/2440/141660The Pierre Auger Observatory, the largest cosmic ray detector in the world, uses a set of fluorescence detectors to measure cosmic rays. However, clouds can enter the field of view of these fluorescence detectors and disrupt cosmic ray measurements, leading to unreliable results. Previously, infrared cloud cameras were placed at each site to monitor the presence of clouds but they became difficult and expensive to maintain. In fact, most of these infrared cloud cameras are no longer operational. As a byproduct, the fluorescence detectors are capable of measuring the brightness of the night sky background-light. In the presence of clouds, the night sky background-light appears to dim as the clouds may absorb and scatter light away from the detectors. This motivated the idea behind developing a method to monitor clouds at the observatory using the brightness of the night sky background light. This thesis investigates a method to detect clouds using the data from the fluorescence detectors. This method is then contained within a Python module for accessibility and quick installation and is integrated with version control using GitHub. In addition to the background chapters, this thesis comprises of three main components: Chapter 3 focuses on measuring the night sky background-light. This involves the calculating the photon flux - the quantity we associate with the night sky background-light. These results are then displayed on a plot which can be animated for a range of time values. Chapter 4 describes the night sky background-light cloud estimation method which uses an elevation and time dependent photon flux threshold to classify pixels on the fluorescence detector camera as cloudy or clear. This also includes the displaying the night sky background-light cloud and the infrared cloud on plots which can be animated for a range of time values. Chapter 5 analyses the performance and evaluates the results of the program. This includes comparing the performance of the cloud estimation method with that from the infrared cloud camera over different cloudy events. The optimal parameters such as the tolerance factor - a parameter which determines the sensitivity to cloud detection - is investigated to produce the best results for the evaluation. Chapter 6 describes the data management encountered used in the Light Integrated Cloud Estimation (LICE) module. This includes the discussion of the main SQLite files and SQL queries used for rapid data retrieval. These SQLite files were implemented to reduce the dependencies used for the Python module and greatly improved the workflow of the module. Chapter 7 describes the LICE module in detail, which includes the design of the module, the main programs which are part of the package, their usage and the possible future improvements. Finally, we discuss the installation of the LICE module, which is easily accessible with the integration of GitHub. Chapter 8 Summarises the main results and outlines some potential avenues for future development and investigation.enThe Pierre Auger Observatoryfluorescence detectorsThesis