MS2H – Toward FAIR and Reproducible Dark Matter Science: Co-Design of Data Infrastructure, AI Workflows, and Community Datasets
Session Chairs
Event Type
Minisymposium
Physics
Computational Methods and Applied Mathematics
TimeMonday, June 2916:00 – 18:00 CEST
LocationBldg. 8 – B 102
DescriptionOrganizer(s): Michela Taufer (University of Tennessee), Amy Roberts (University of Colorado Denver), and Belina von Krosigk (Kirchhoff-Institute for Physics (KIP))
Dark matter constitutes approximately 85% of the universe’s matter, yet its nature remains elusive. Direct detection experiments, though globally deployed, have historically generated data locked within custom formats and non-reproducible software stacks — limiting interdisciplinary analysis and innovation. As AI and machine learning become integral to scientific workflows, ensuring data are FAIR and accompanied by rigorous provenance is essential for reproducibility and trust. This minisymposium uses dark matter science as a concrete case study in co-designing data infrastructure and AI-ready workflows. Centered on the National Science Data Fabric (NSDF) and the SuperCDMS collaboration, three talks address this challenge from complementary angles: rethinking data formats and computing using DELight as a testbed for future experiments; establishing metadata standards that make AI-driven analyses reproducible and auditable; and building an open community dataset by converting proprietary detector data into accessible, machine-learning-ready formats. Together, these case studies show how federated, inclusive data ecosystems broaden participation and strengthen confidence in scientific results — directly aligned with the PASC26 theme, “Building Trust in Science through HPC Co-Design.”
Dark matter constitutes approximately 85% of the universe’s matter, yet its nature remains elusive. Direct detection experiments, though globally deployed, have historically generated data locked within custom formats and non-reproducible software stacks — limiting interdisciplinary analysis and innovation. As AI and machine learning become integral to scientific workflows, ensuring data are FAIR and accompanied by rigorous provenance is essential for reproducibility and trust. This minisymposium uses dark matter science as a concrete case study in co-designing data infrastructure and AI-ready workflows. Centered on the National Science Data Fabric (NSDF) and the SuperCDMS collaboration, three talks address this challenge from complementary angles: rethinking data formats and computing using DELight as a testbed for future experiments; establishing metadata standards that make AI-driven analyses reproducible and auditable; and building an open community dataset by converting proprietary detector data into accessible, machine-learning-ready formats. Together, these case studies show how federated, inclusive data ecosystems broaden participation and strengthen confidence in scientific results — directly aligned with the PASC26 theme, “Building Trust in Science through HPC Co-Design.”
Presentations
