Karlsruhe 2024 – scientific programme

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T: Fachverband Teilchenphysik

T 46: Di-Higgs 1 (bbττ)

T 46.3: Talk

Tuesday, March 5, 2024, 16:30–16:45, Geb. 30.41: HS 1

A neural network based regression of the neutrinos in H→ττ decays in the context of the CMS resonant HH→bbττ analysis — Philip Keicher, •Tobias Kramer, Marcel Rieger, and Peter Schleper — Universität Hamburg

The CMS resonant HH→bbττ analysis searches for the decays of heavy spin 0/2 resonances to a pair of Higgs bosons in the bbττ final state. It uses the data collected from 2016-2018 (Run 2) at √s = 13 TeV corresponding to an integrated luminosity of 138 fb⁻¹. One important challenge is to reconstruct the kinematics of the two Higgs bosons. Especially in the H→ττ decay a large fraction of the energy is lost, because the neutrinos resulting from the τ decays are not detected. This talk presents studies on how to regress the full HH system using deep neural networks and the effects of including an additional classification part as well as parameterized (in mass and spin of the resonance) approaches.

Keywords: Di-Higgs; Machine Learning