From Detection to Interpretation: Visual Analytics for Radionuclide Monitoring

National Data Centres (NDCs) responsible for nuclear weapon test verification face a critical analytical challenge: systematically identifying radionuclide samples that may share common source regions. Current tools from the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) fragment workflows across separate applications for spectrum analysis, timeseries visualization, and atmospheric transport modeling, forcing analysts to manually compare samples through ad hoc Excel-based methods. We present RaDIA (Radionuclide Data Integration and Analysis), a visual analytics dashboard that integrates sample metadata, isotopic measurements, and source-receptor sensitivity (SRS) fields into coordinated multiple views. RaDIA implements a spatial overlap detection algorithm that quantifies associations between samples by calculating shared grid cells in backward atmospheric trajectories, visualized through interactive maps, temporal Sankey diagrams, and sortable tables. Through Research-through-Design with three NDCs, we validated that RaDIA addresses documented workflow gaps by consolidating fragmented tools, reducing cognitive load, and enabling systematic sample association. Our work demonstrates how domain-specific visual analytics can democratize analytical capacity for smaller NDCs in high-stakes verification contexts.