6533b82bfe1ef96bd128d74d

RESEARCH PRODUCT

Going standalone and platform-independent, an example from recent work on the ATLAS Detector Description and interactive data visualization

Vakhtang TsulaiaEdward MoyseJ. BoudreauPaul Gessinger-befurtPaul Gessinger-befurtSebastian Andreas MerktAndreas SalzburgerRiccardo-maria Bianchi

subject

Engineering drawing010308 nuclear & particles physicsbusiness.industryAtlas detectorPhysicsQC1-999ATLAS experimentDetectorcomputer.software_genre01 natural sciencesVisualizationSoftware frameworkSoftware portabilityData visualizationKernel (image processing)0103 physical sciences010306 general physicsbusinesscomputerParticle Physics - Experiment

description

Until recently, the direct visualization of the complete ATLAS experiment geometry and final analysis data was confined within the software framework of the experiment. To provide a detailed interactive data visualization capability to users, as well as easy access to geometry data, and to ensure platform independence and portability, great effort has been recently put into the modernization of both the core kernel of the detector description and the visualization tools. In this proceedings we will present the new tools, as well as the lessons learned while modernizing the experiment’s code for an efficient use of the detector description and for user-friendly data visualization. Until recently, the direct visualization of the complete ATLAS experiment geometry and physics objects was confined within the software framework of the experiment. To provide a detailed interactive data visualization capability to users, as well as easy access to geometry data, and to ensure platform independence and portability, great effort has been recently put into the modernization of both the core kernel of the detector description and the visualization tools. In this proceedings we will present the new tools, as well as the lessons learned while modernizing the experiment’s code for an efficient use of the detector description and for user-friendly data visualization.

10.1051/epjconf/201921402035http://cds.cern.ch/record/2649301