The goal of this project is to address those challenges using cutting edge stochastic optimization and machine learning methods. In particular, this project aims to improve the precision of mass measurements of the top quark, the largest observed subatomic particle, from data collected on the Tevatron. Doing so requires improving the process of collision selection, wherein collisions resulting in top quarks are separated from those resulting in other particles. Previous results have already demonstrated that stochastic optimization techniques can significantly improve collision selection, yielding more precise mass measurements that would otherwise require literally millions of dollars and hundreds of person-years to generate. However, much work remains to be done to improve this process and rigorously compare performance among different optimization and supervised learning techniques.

The resulting methodology will then be applied to collision selection problems resulting from new data generated by the LHC, with the goal of aiding the search for the Higgs boson, a critically important but as yet unobserved particle theorized to give mass to all other particles through its interactions.