2018/06/30 by Dianne Cook, Ursula Laa, German Valencia +1
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Computational Physics and Python Applications #Computer science #Data mining #Data visualization #Engineering #Geometry #Mathematics #Particle physics theoretical and experimental studies #Plane (geometry) #Principal (computer security) #Principal component analysis #Quantum Chromodynamics and Particle Interactions #Sensitivity (control systems) #Subspace topology #Visualization #hep-ex #hep-ph #physics.data-an
paper · pdf · doi:10.1140/epjc/s10052-018-6205-2
Format of the animations changed for easier viewing
arxiv created 2018/07/23 · openalex publication_date 2018/09/01 · arxiv updated 2018/10/17 · openalex created_date 2020/11/23 · openalex updated_date 2026/08/05
A recent paper on visualizing the sensitivity of hadronic experiments to nucleon structure (Wang et al. in arXiv:1803.02777 , 2018) introduces the tool PDFSense which defines measures to allow the user to judge the sensitivity of PDF fits to a given experiment. The sensitivity is characterized by high-dimensional data residuals that are visualized in a 3-d subspace of the 10 first principal components or using non-linear embeddings. We show how a tour, a dynamic visualisation of high dimensional data, can extend this tool beyond 3-d relationships. This approach enables resolving structure orthogonal to the 2-d viewing plane used so far, and hence finer tuned assessment of the sensitivity.