  In this article, we explore machine learning techniques using support vector
machines with two novel approaches: exotic and physics-informed support vector
machines. Exotic support vector machines employ unconventional techniques such
as genetic algorithms and boosting. Physics-informed support vector machines
integrate the physics dynamics of a given high-energy physics process in a
straightforward manner. The goal is to efficiently distinguish signal and
background events in high-energy physics collision data. To test our
algorithms, we perform computational experiments with simulated Drell-Yan
events in proton-proton collisions. Our results highlight the superiority of
the physics-informed support vector machines, emphasizing their potential in
high-energy physics and promoting the inclusion of physics information in
machine learning algorithms for future research.
