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From prototypes to large scale detectors: how to exploit the Gaussino simulation framework for detectors studies, with a detour into machine learning

May 11, 2023, 12:00 PM
Chesapeake Meeting Room (Norfolk Waterside Marriott)

Chesapeake Meeting Room

Norfolk Waterside Marriott

235 East Main Street Norfolk, VA 23510
Oral Track 5 - Sustainable and Collaborative Software Engineering Track 5 - Sustainable and Collaborative Software Engineering


Mazurek, Michał (CERN)


Gaussino is a new simulation experiment-independent framework based on the Gaudi data processing framework. It provides generic core components and interfaces to build a complete simulation application: generation, detector simulation, geometry, monitoring, and saving of the simulated data. Thanks to its highly configurable and extendable components Gaussino can be used both as a toolkit and a stand-alone application. It provides implementations for software components widely used by the High Energy Physics community, e.g. Pythia and Geant4. Geometry layouts can be provided through DD4Hep or experiment-specific software. A built-in mechanism is available to define simple volumes at configuration time and ease the development cycle. Inspections of the geometry and simulated data can be performed through Geant4 visualization driver accessible in Gaussino. It is also possible to save objects for visualising them a posteriori with Phoenix. We will show how Gaussino can be first used to try out new detector ideas and increasing the complexity of the geometry and physics processes can provide the foundation for a complete experiment simulation where the same detector can be used and its physics performance evaluated. The possibility of retrieving custom information from any place in the detector also allows to obtain samples for proposed additions to experimental setup as well as training datasets for studies involving machine learning, such as fast simulation models, for which use Gaussino provides a dedicated interface.

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