Karlsruhe 2024 – scientific programme

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T: Fachverband Teilchenphysik

T 72: BSM Higgs 3 (extended Higgs sectors)

T 72.6: Talk

Wednesday, March 6, 2024, 17:15–17:30, Geb. 30.41: HS 3

A novel machine learning-based background estimation for the X → SH → 4b analysis at the ATLAS experiment — •Malin Horstmann, Nicole Hartman, and Lukas Heinrich — Technical University Munich, Germany

The search for additional scalar particles has been part of the ATLAS physics program since the Higgs discovery. The work presented has been done within the X → SH → 4b analysis at the ATLAS experiment, which searches for two additional scalars in the dominant decay mode of the Higgs. In order to interpret the data, an estimation of the expected background is necessary. We present a novel machine learning-based background estimation technique that uses a normalising flow and a Gaussian process. A particular focus will be on the propagation of the systematic uncertainties related to the neural network model.

Keywords: ATLAS; BSM Physics; Machine Learning