Abstract
Colliders allow physicists to probe the previously unknown world of sub-atomic physics employing observations of exotic particles through high-energy collisions. Physics communities regularly rely on hand-crafted, high-level features in conjunction with shallow machine-learning packages to accurately identify particles produced in collisions. This process proves excessive and time-consuming. This work provides an innovative means of solving the problem of accurate identification of Higgs boson particles through state-of-the-art, semi-supervised learning methods, and data collected by the European Centre for Nuclear Research. This research demonstrates how using semi-supervised learning techniques, specifically weight-averaged consistencies and data abstraction methods, alleviates the need for fully labeled datasets in accurate identification. Furthermore, it is demonstrated how deep semi-supervised learning models automatically extrapolate high-level features from the data given.
How to Cite
Spradlin, C., (2020) “Application of Semi-Supervised Machine Learning Methods Towards the Identification of the Higgs Boson”, Capstone, The UNC Asheville Journal of Undergraduate Scholarship 33(1).
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