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15–17 Jan 2020
Kimmel Center for University Life
America/New_York timezone

Using machine learning to constrain the Higgs total width

17 Jan 2020, 17:20
20m
KC 914 (Kimmel Center for University Life)

KC 914

Kimmel Center for University Life

60 Washington Square S, New York, NY 10012

Speakers

Dylan Sheldon Rankin (Massachusetts Inst. of Technology (US)) Cristina Ana Mantilla Suarez (Johns Hopkins University (US))

Description

Despite the discovery of the Higgs boson decay in five separate channels many parameters of the Higgs boson remain largely unconstrained. In this paper, we present a new approach to constraining the Higgs total width by requiring the Higgs to be resolved as a single high pT jet and measuring the visible and partially visible Higgs boson cross section. This approach complements existing approaches from the off-shell technique and lepton colliders. To measure the Higgs boson decays, we rely on new ideas from machine learning for jet classification and a modified jet reconstruction that uses a dedicated missing energy regression. With some assumptions, this approach is found to be capable of yielding similar sensitivity to the off-shell projections with the full High Luminosity-LHC dataset. We outline the theoretical and experimental limitations of this approach and present a path towards making a truly model-independent measurement of the Higgs boson total width.

Primary authors

Philip Coleman Harris (Massachusetts Inst. of Technology (US)) Dylan Sheldon Rankin (Massachusetts Inst. of Technology (US)) Cristina Ana Mantilla Suarez (Johns Hopkins University (US))

Presentation materials