Identification and Classification of Beam Loss Patterns in the Large Hadron Collider

The Large Hadron Collider, is the largest particle accelerator ever built, achieving record beam energy and beam intensity. Beam losses are unavoidable and can risk the safety of accelerator's components. Beam loss maps are used to validate the collimation system, designed to protect the accelerator against beam losses. The complexity of this system requires well defined inspection methods and well defined case studies that ensure normal operation and efficient performance evaluation. In this work, enhancements are proposed to the existing validation methods with extensions towards automating the inspection mechanisms, introducing pattern recognition and statistical learning methods.