Elements: Portable Machine Learning Models for Experimental Nuclear Physics

Davidson College, Davidson NC

Investigators

Abstract

Experiments in nuclear physics produce increasingly vast amounts of data that require novel and sophisticated methods of data analysis to support the process of scientific discovery. The field of machine learning (ML), which sits at the intersection of computer science, statistics and mathematics, is concerned with developing algorithms and systems that improve at a certain task with accrued experience. In contrast to traditional software systems that rely on explicit instructions from the programmer, ML systems are able to automatically derive insights from large datasets. This approach has had a significant impact on many disciplines—indeed, on nearly every area of society—and nuclear physics is no exception. However, when one surveys the myriad ways in which ML is used in the physics community, a notable trend emerges, namely, the tendency to construct bespoke models from scratch for each new application. This is a task that requires considerable technical expertise and access to large volumes of data that have been painstakingly annotated by humans from which the statistical models can “learn”. Recent advances in ML have focused on minimizing the reliance on large-scale hand-labeling of data. This project centers on building models using these new techniques for various nuclear physics experiments at the Facility for Rare Isotope Beams (FRIB) in Michigan that will then be released to the scientific community. These models can be adapted through a process offine-tuning by end-users for a variety of downstream applications. The models are developed using unlabeled data from three particle detector systems at FRIB – the Active-Target Time Projection Chamber (AT-TPC), the Summing NaI (SuN) detector, and the SAMURAI Pion Reconstruction and Ion Tracker (SPiRIT) Time Projection Chamber. The models are evaluated on key analysis and fitting tasks that have been identified by our collaborators at FRIB. Undergraduate students play a central role in executing the research agenda. By engaging them in cutting-edge research in partnership with physicists at a national facility, students are prepared for impactful careers in STEM, both in academia and in the broader workforce. Additionally, this project has partnered with the Research in Science Experience program at Davidson College and provides full-summer research experiences in our lab to students from groups historically excluded from the sciences. Pretrained models are built using state-of-the-art self-supervised machine learning (ML) methods to support physicists who would like to solve a variety of analysis tasks in nuclear physics experiments. These models support users of three detector systems that are developed and maintained by groups of scientists at the Facility for Rare Isotope Beams (FRIB) in Michigan. FRIB is a nuclear science user facility that came online in the summer of 2022. FRIB’s users investigate nuclear properties across the nuclear landscape and require a variety of different experimental setups throughout the year. Since parameters such as beam composition and energy change between experiments in a given detector, the users of the facility need to train new ML models afresh for each experiment. Pretrained models can be quickly adjusted and adapted for users' desired use cases, reducing the burden on experimentalists. This work builds on industry-standard and industry-leading ML software and libraries, such as pytorch and tensorflow, and the pretrained models are openly available to the community, together with scripts for fine-tuning them for specific applications. The models are also integrated into standard software used by domain scientists. This work brings together experimental nuclear physicists, computer scientists and data scientists to create a tight-knit collaboration across disciplines. Undergraduate students play a central role in executing the research agenda. By engaging them in cutting-edge research in partnership with physicists at a national facility, students are prepared for impactful careers in STEM, both in academia and in the broader workforce. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier program 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.