Advancements in Computing and Simulation Techniques for the HIBEAM-NNBAR Experiment
Bernhard Meirose, Jorge Amaral, Alexander Burgman, Matthias Holl, Ernesto Kemp, Adam Kozela, David Milstead, Andr\'e Nepomuceno, Anders Oskarsson, Krzysztof Pysz, Valentina Santoro, Tiago Quirino, Blahoslav Rataj, Gabriel Silva, Samuel Silverstein, Magnus Wolke, Lucas {\AA}strand
TL;DR
This paper discusses recent advancements in computing and simulation techniques, including machine learning and detailed modeling, to enhance the HIBEAM-NNBAR experiment's search for baryon number violation and dark matter.
Contribution
It introduces new computational methods and simulation models specifically developed for the HIBEAM-NNBAR experiment to improve detection sensitivity and analysis accuracy.
Findings
Machine learning improves event selection efficiency.
Fast parametric simulations enable rapid detector studies.
Detailed modeling enhances understanding of detector responses.
Abstract
The HIBEAM-NNBAR program is a proposed two-stage experiment at the European Spallation Source focusing on searches for baryon number violation processes as well as ultralight dark matter. This paper presents recent advancements in computing and simulation, including machine learning for event selection, fast parametric simulations for detector studies, and detailed modeling of the time projection chamber and readout electronics.
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