ABSTRACT

Detecting dark matter in the lab would be transformational for physics, and such a difficult measurement requires providing a foundation for early-career scientists in advanced data analytics. The science question being pursued is generally acknowledged to be one of the most important questions in particle physics and astrophysics and is key to understanding what makes up the vast majority of the universe. Effective training in good computing practices is required for major research advances in this field. The project will consolidate and strengthen training efforts in scientific software development and data analysis within the field of experimental dark matter research. Scientifically, the training will enable discovery that will come from a world-wide effort consisting of hundreds of junior scientists searching for extremely-rare events on petabytes of data - effectively looking for a needle in a haystack the size of Texas. The project serves the national interest as stated by NSF's mission to promote the progress of science by preparing a workforce trained in cyberinfrastructure, and will support STEM disciplines with critical software training that is much needed both in scientific fields and in industry.

The dark matter community consists of more than a thousand scientists at the frontier of ultra-rare event searches whose efforts support more than twenty different experiments. Searching for dark matter in multiple ways has resulted in disparate and often inadequate computational training. This project addresses the training problem to maximize impact across the field. Representing three leading dark matter experiments, the project investigators will develop educational material and training workshops for systematic data science education to ensure early career scientists can harness the data volumes being produced by modern experiments. The project will host two training workshops per year, toward the goal of developing a community of instructors and also a set of training materials for free distribution and reuse. Beyond domain-specific training in rare-event searches, foundational computational knowledge will be developed when necessary by working with partners such as the Software and Data Carpentries. The project includes specific goals to engage women and underrepresented minorities in the training activities and broaden their advancement within the field. Additionally, the project will provide mentors for advanced students through hackathons. These trainings will directly contribute to broader STEM workforce development while training students such that they can pursue careers in data science and/or data-intensive research. This project is funded by the Office of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering and the Division of Physics in 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.

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