Probing the Triple Higgs Self-Interaction at the Large Hadron Collider.

Area of Science:

• High Energy Physics
• Particle Physics
• Collider Physics

Background:

• The double Higgs (hh) production is a key process for understanding the Higgs boson potential.
• Discovering hh production is experimentally challenging due to its low cross-section and complex final states.
• Existing methods often rely on complex machine learning algorithms.

Purpose of the Study:

• To propose a novel and efficient kinematic method for the discovery of double Higgs production.
• To enhance the sensitivity of searches for double Higgs events in the ℓ^{+}ℓ^{-}bb[over ¯]+E_{T} final state.
• To provide a method easily generalizable to other Higgs production channels.

Main Methods:

• Utilized recently developed kinematic variables.
• Incorporated "Topness" variable to suppress top quark pair production background.
• Employed "Higgsness" variable to enhance signal identification for double Higgs events.

Main Results:

• Achieved a significant increase in discovery sensitivity for double Higgs production.
• Demonstrated superior performance compared to analyses using boosted decision trees and neural networks.
• Validated the effectiveness of the "Topness" and "Higgsness" variables in kinematic analysis.

Conclusions:

• The proposed kinematic method offers a more sensitive and efficient approach to discovering double Higgs production.
• This method can be readily extended to resonant double Higgs production and other nonresonant channels.
• The technique provides a valuable tool for future investigations in Higgs physics.