Speaker
Edmar Egidio Purcino De Souza
(UFBA)
Description
A number of flagship analyses in the ATLAS experiment rely on electron and photon triggers to efficiently record events of interest. During Run 3, these triggers have undergone significant developments, including the integration of advanced machine learning techniques, to achieve high efficiency while controlling background rates and operating within the constraints of the trigger hardware. This contribution presents the design, optimisation, deployment, and validation of the ATLAS Run 3 electron and photon triggers, and discusses their impact on key physics analyses.
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Authors
Borut Paul Kersevan
(Jozef Stefan Institute (SI))
Edmar Egidio Purcino De Souza
(UFBA)