Experimental Research in Elementary Particle Physics

About This Grant

Understanding the fundamental forces of nature remains one of the most profound goals of modern science. The discovery of the Higgs boson has highlighted a long-standing mystery: why is gravity so much weaker than the other fundamental forces? Many models that attempt to explain the structure of the universe suggest the existence of new, undiscovered particles and interactions. With this award, researchers at Rutgers University are embarking on an innovative project at the Large Hadron Collider (LHC) to search for signs of new physics that may bridge this gap. By using advanced machine learning techniques and novel data analysis strategies, the team aims to uncover faint signals of new physics processes. Looking to the future, the group has a critical role in both building advanced detector components and developing the technologies needed to analyze the vast increase in collision events at the upgraded High Luminosity LHC. These efforts will enable scientists to probe fundamental questions about the universe more precisely than ever before, hopefully shedding light on the origin of mass and the nature of dark matter. The group also prioritizes the mentoring of young people: postdocs, graduate and undergraduate students, as well as high school students through the QuarkNet program which engages them in hands-on particle physics research. This project aligns with the NSF mission by promoting scientific progress, supporting STEM education, and potentially reshaping our understanding of the universe. This award will allow the Rutgers group, a long-standing member of the CMS experiment at the LHC, to conduct targeted searches for beyond-the-Standard-Model (BSM) physics with a focus on low-mass signatures accessible through nontraditional datasets such as Scouting and Parking streams. These enable exploration of regions previously considered inaccessible at hadron colliders. The group applies advanced machine learning techniques—including adversarial networks for background suppression, autoencoders for anomaly detection, and deep learning-based image classification—to enhance real-time and offline event selection sensitivity under high-background conditions. The project also involves R&D on hardware-level triggering, fast reconstruction algorithms, and interpretable AI models with robust control of systematics. In support of the High-Luminosity LHC upgrade, the group leads module production, assembly, and testing for the CMS Outer Tracker, and contributes to the design of the Track Finder and Global Track Trigger systems for identifying high transverse momentum tracks. These integrated efforts are designed to extend the LHC’s sensitivity to electroweak- and sub-electroweak-scale phenomena, enabling precision tests of theoretical models and potential discovery of new particles or interactions. 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.