Authors: Norm Buchanan, Steven Calvez, and Derek Doyle (Colorado State University); V. Hewes, Alexander Himmel, James Kowalkowski, Andrew Norman, and Marc Paterno (Fermi National Accelerator Laboratory); Tom Peterka (Argonne National Laboratory (ANL)); Saba Sehrish (Fermi National Accelerator Laboratory); Alexandre Sousa and Tarak Thakore (University of Cincinnati); and Orcun Yildiz (Argonne National Laboratory (ANL))
Abstract: NOvA is a world-leading neutrino physics experiment that is making measurements of fundamental neutrino physics parameters and performing searches for physics beyond the Standard Model. These measurements must leverage high performance computing facilities to perform data intensive computations and execute complex statistical analyses. We outline the NOvA analysis workflows we have implemented on NERSC Cori and Perlmutter systems. We have developed an implicitly-parallel data-filtering framework for high energy physics data based on pandas and HDF5. We demonstrate scalability of the framework and advantages of an aggregated monolithic dataset by using a realistic neutrino cross-section measurement. We also demonstrate the performance and scalability of the computationally intensive profiled Feldman-Cousins procedure for statistical analysis. This process performs statistical confidence interval construction based on non-parametric Monte Carlo simulation and was applied to the NOvA sterile neutrino search. We show the NERSC Perlmutter system provides an order of magnitude computing performance gain over Cori.
Best Poster Finalist (BP): yes
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