Elements: RAD Discoveries for Fundamental Physics

This project supports a diverse team of physicists and computer scientists to leverage recent advancements in AI and newly available particle detector capabilities in order to develop new computationally- and energy-efficient technology that is capable of detecting rare and unpredictable patterns in data with unprecedented intelligence. Such technology finds wide applicability in industry, medicine, national security, but also fundamental physics research. The latter is a key focus of the project, where the developed infrastructure enables new searches for yet-undiscovered physics phenomena using state-of-the-art particle detectors at national and international particle accelerator facilities. These searches represent a major departure from the methods that scientists have been applying for decades in particle physics experiments to discover new phenomena, in that they are no longer confined within an existing and limited understanding of how the Universe works, or a prior expectation for how rare and unpredictable patterns in data may manifest. The developed technology allows the data itself, rather than prior knowledge, to guide new discoveries about how Nature works at its most fundamental level, and aims to revolutionize the way new discoveries are made.This project aims to transform fundamental discoveries in particle and astro-particle physics by developing new Cyberinfrastructure (CI) for real-time anomaly detection (“RAD”). The newly developed CI enables searches that can uncover “unknown” physics through rare, unpredictable phenomena. Traditionally, discoveries of rare processes through particle and astro-particle experiments have relied on scientists’ ability to accurately predict new physics phenomena, and to subsequently selectively look for them within the data by using algorithms trained on predicted unknowns. In a new era of scientific discovery, driven by unprecedented data statistics and Artificial Intelligence (AI) advances, scientists instead may now use AI-powered tools that let the data guide expectation to selectively identify rare and unpredictable signatures that may lie within the data itself, and which may be signatures of new fundamental physics phenomena in nature. The project brings together physicists and computer scientists at Columbia and Princeton, including students, post-doctoral researchers, and CI professionals, and additional collaborators at other US institutions and national labs, to develop anomaly detection algorithms that are executable on resource constrained platforms, commonly employed as on-detector data processing devices at particle physics experiments. Through targeted developments and demonstrations at existing particle detector facilities, the ultimate goal of this project is to develop advanced CI that is energy-efficient, reliable, scalable, and expandable, and thus applicable to trigger systems of current and future generation experiments in the intensity, energy, and cosmic frontiers of particle physics. By design, the delivered CI is capable of fundamental discoveries that are beyond what is envisioned with current trigger frameworks, and has the potential of revolutionizing the way particle and astro-particle physics discoveries are made. The developed CI promises to be broadly applicable to other high-throughput data-selection applications that require real-time, intelligent data processing for the purposes of discovering anomalies in data. These applications can range from neuroscience, to medical imaging, to satellite imaging, cybersecurity, etc.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier in the Division of Physics within the Directorate for Mathematical and Physical Sciences.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.