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Project # 22
Gamma-Ray Detection: Machine Learning on MAGIC Telescope Data
| Team Lead: | Aaron Birdsong |
| Other Team Members: | Sajid Bin Arif | Somik Biswas | Brendon Kidd | Michael Willimam, Jr Kubash | Tyler Ngyuen | Jed Nikolai Moya |
| Faculty Advisor(s): | Dr. Aera Kim LeBoulluec |
| Department: | IMSE |
| Project Type: | Other Undergraduate |
| Title: | Gamma-Ray Detection: Machine Learning on MAGIC Telescope Data |
| Abstract: | Analyzing high-energy gamma rays is essential for understanding extreme cosmic events, but distinguishing these gamma rays from background cosmic rays can be quite challenging. The MAGIC (Major Atmospheric Gamma Imaging Cherenkov) Telescope plays a key role in this analysis by capturing data on electromagnetic showers. This includes various parameters such as length, width, size, asymmetry and distance. We will apply linear regression techniques in Python to analyze this data. Linear regression models can predict important parameters like gamma-ray energy by utilizing features such as shape, concentration, and distance. By refining energy estimation and event classification, these models can enhance the accuracy of data analysis. This is especially valuable for differentiating gamma-ray showers from hadronic showers caused by cosmic rays. Distinguishing between these two types of events is crucial for advancing our understanding of high-energy cosmic phenomena. Applying linear regression provides a powerful tool for processing complex datasets, improving the precision of gamma-ray detection, and contributing to the study of the universe’s most extreme and energetic events. |
| Poster: | View Poster #22 (PDF) |
| Judges Scoring: | Submit Evaluation (judges only) |