NSF Award
2412880
This award funds the research activities of Professor Zhengkang Zhang at the University of Utah.<br/><br/>Our understanding of Nature at the most fundamental level is based on effective field theory (EFT), a theoretical framework that combines quantum physics, relativity, and the fact that Nature is organized by scales (little things affect big things, which in turn affect even bigger things). Agreement between EFT predictions and experimental data underlies the success of elementary particle physics, and has done so over the past century. However, theoretical puzzles remain, and many of them are associated with small numbers. The Higgs boson, for example, has a mass that is much smaller than our expectations from EFT reasoning. Recent studies have also revealed many more small-number mysteries in the form of surprising cancellations in EFT calculations of particle physics models. Under this award, Professor Zhang will develop new theoretical tools to resolve some of these mysteries, and exploit recent developments in machine learning (ML) to advance EFT calculations. Professor Zhang will also perform new calculations to better evaluate the discovery potential of ongoing experiments searching for dark matter. This project advances the national interest by promoting the progress of science in one of its most fundamental directions: the understanding of new theoretical principles and discovery of new physical laws. This project is also envisioned to have significant broader impacts. Professor Zhang will build novel connections between EFT and ML which will improve our understanding of ML and promote more responsible use of artificial intelligence in many aspects of society. He will also involve students and postdocs in his research, thereby providing critical training for junior physicists beginning research in this field. He will also engage in outreach activities to share his research with the public and increase scientific literacy.<br/><br/>More technically, Professor Zhang will develop new EFT tools, including geometric and functional methods, and seek to explain “magic zeroes” (surprising cancellations) in EFT calculations involving higher-dimensional operators in the Standard Model (SM). He will, in particular, use functional methods to systematically investigate the conditions under which magic zeroes arise, and further develop geometric frameworks in which EFT amplitudes are covariant under general field redefinitions. This research will at the very least expand the phenomenologist toolbox which will enable us to better understand the structures of SMEFT (Standard Model EFT) and make more efficient use of SMEFT for physics beyond the Standard Model. Optimistically, this might also lead to new insights into the hierarchy problem. Meanwhile, Professor Zhang will thoroughly explore a nascent correspondence between neural networks and field theories, and develop novel ML-inspired approaches for field theory simulations. Finally, he will extend sub-GeV dark matter direct-detection calculations in order to better quantify halo uncertainties and directional sensitivity in phonon-based experiments.<br/><br/>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.
