Stochastic Optimization for Collision Selection in High Energy Physics

Abstract

Artificial intelligence has begun to play a critical role in basic science research. In high energy physics, AI methods can aid precision measurements that elucidate the underlying structure of matter, such as measurements of the mass of the top quark. Top quarks can be produced only in collisions at high energy particle accelerators. Most collisions, however, do not produce top quarks and making precise measurements requires culling these collisions into a sample that is rich in collisions producing top quarks (signal) and spare in collisions producing other particles (background). Collision selection is typically performed with heuristics or supervised learning methods. However, such approaches are suboptimal because they assume that the selector with the highest classification accuracy will yield a mass measurement with the smallest statistical uncertainty. In practice, however, the mass measurement is more sensitive to some backgrounds than others. This paper presents a new approach that uses stochastic optimization techniques to directly search for selectors that minimize statistical uncertainty in the top quark mass measurement. Empirical results confirm that stochastically optimized selectors have much smaller uncertainty. This new approach contributes substantially to our knowledge of the top quark's mass, as the new selectors are currently in use selecting real collisions.