2021/03/14 by Ursula Laa, German Valencia, G. Valencia +2 · 1 citation
Computer Science · Mathematics · Medicine · Physics and Astronomy · #Anomaly Detection Techniques and Applications #Applications (stat.AP) #Data Analysis #Data-Driven Disease Surveillance #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #Statistics and Probability (physics.data-an) #hep-ex #hep-ph #physics.data-an #stat.AP
paper · pdf · doi:10.48550/arxiv.2103.07937
48 pages, 30 figures, version to appear in EPJ Plus
openalex publication_date 2021/03/14 · arxiv created 2021/12/20 · arxiv updated 2021/12/21 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
We introduce the interactive tool pandemonium to cluster model predictions that depend on a set of parameters. The model predictions are used to define the coordinates in observable space which go into the clustering. The results of this partitioning are then visualized in both observable and parameter space to study correlations between them. The tool offers multiple choices for coordinates, distance functions and linkage methods within hierarchical clustering. It provides a set of diagnostic statistics and visualization methods to study the clustering results in order to interpret the outcome. The methods are most useful in an interactive environment that enables exploration, and we have implemented them with a graphical user interface in R. We demonstrate the concepts with an application to phenomenological studies in flavor physics in the context of the so-called B anomalies.