Preserving physically important variables in optimal event selections: A case study in Higgs physics

Analyses of collider data, often assisted by modern Machine Learning methods, condense a number of observables into a few powerful discriminants for the separation of the targeted signal process from the contributing backgrounds. These discriminants are highly correlated with important physical obse...

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Veröffentlicht in:	arXiv.org 2020-06
Hauptverfasser:	Windischhofer, Philipp, Zgubic, Miha, Bortoletto, Daniela
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Higgs bosons Machine learning Physics - High Energy Physics - Phenomenology Quarks Sensitivity analysis Signal processing Standard model (particle physics)
Online-Zugang:	Volltext
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