The rapid technological advances of the last two decades have ushered in an era of data-rich science for several disciplines. One such discipline is astroparticle physics, where researchers aim to discover what our Universe is made of by trying to directly detect Dark Matter. This discovery can be hastened if data science tools are used to extract significant domain-specific information from data, and to reliably test scientific hypotheses at scale. The overarching goal of this two-year project is to lay the groundwork for incorporating scientific knowledge into machine learning and data science methods in the context of scientific disciplines in which discovery requires effective, efficient analysis of lots of noisy data gathered by multiple imperfect sensors. In doing so, it not only advances the state-of-the-art in data science, machine learning, and astrophysics, but it also has the potential to accelerate data-driven discoveries in other scientific disciplines where data shares similar characteristics.
This project will develop innovative domain-enhanced data science methods that will be based on probabilistic graphical models and graph-regularized inverse problems. Using the leading astroparticle experiment XENON as a test bed, the investigators will explore and demonstrate approaches for incorporating domain knowledge into machine learning and data science methods. In doing so, the investigators will address major data-analysis challenges in the context of dark matter identification. Additionally, the investigators will invest significant effort reaching out to other data-intensive science communities, such as materials science, oceanography, and meteorology, that can benefit from the new methods and ideas.
This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.
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.
