Theoretical Particle Physics in the Data-Driven Era

This award funds the research activities of Professor Kyoungchul (KC) Kong at the University of Kansas. Modern particle physics experiments generate enormous volumes of complex data that are essential for understanding the fundamental building blocks of matter, the nature of dark matter, and the evolution of the universe. As next-generation experiments increase in scale and sophistication, new computational approaches are needed to extract scientific discoveries from these datasets. Under this award, Professor Kong will develop innovative methods that combine artificial intelligence, machine learning, and quantum information science to address these challenges and expand the discovery potential of current and future experiments. This research advances the national interest by promoting the progress of science through the discovery and understanding of fundamental physical laws, while also strengthening US leadership in advanced computing, data science, and quantum technologies. The project will provide research opportunities and training for undergraduate and graduate students, contribute to curriculum development in emerging areas of AI and quantum computing, and help prepare skilled workforce at the interface of physics, computation, and information science. More technically, this project investigates three interconnected research directions in theoretical particle physics. First, Professor Kong will develop quantum algorithms for data analysis, event reconstruction, and anomaly detection, and will explore quantum sensing platforms based on superconducting qubits and trapped ions to study radiative decays of neutrinos and dark matter particles. Second, he will conduct collider phenomenology studies of top quark properties, Higgs boson interactions, and signatures of physics beyond the Standard Model at the Large Hadron Collider and future collider facilities, with particular emphasis on combinatorial reconstruction challenges and probes of CP violation in the Higgs-top sector. Third, he will investigate multi-component dark matter scenarios through numerical simulations and cosmological observables, connecting astrophysical measurements with laboratory searches for light dark matter. By integrating AI-driven feature engineering, quantum computing techniques, and modern theoretical methods, this research will provide new tools for interpreting experimental data, identifying signatures of new physics, and advancing the scientific goals identified by the Snowmass process, the National Academies, and the Particle Physics Project Prioritization Panel (P5). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.