Data-Driven Discovery Strategy for Standard Model Effective Field Theory Searches.

Area of Science:

• High Energy Physics
• Particle Physics
• Theoretical Physics

Background:

• The Standard Model (SM) is the current best description of fundamental particles and forces.
• Deviations in collider data suggest the possibility of new physics beyond the SM.
• Existing global analyses may lack the sensitivity to detect subtle indirect signs of new physics.

Purpose of the Study:

• To develop a novel strategy for uncovering indirect signs of new physics.
• To improve sensitivity in detecting new physics compared to traditional methods.
• To systematically identify Standard Model Effective Field Theory (SMEFT) scenarios that explain observed deviations.

Main Methods:

• Utilizing the Standard Model Effective Field Theory (SMEFT) framework.
• Employing genetic algorithms to navigate high-dimensional operator spaces.
• Identifying operator subsets that improve agreement with collider data without prior ultraviolet (UV) assumptions.
• Validating the approach on Large Hadron Collider (LHC) and Large Electron-Positron Collider (LEP) data.
• Performing closure tests with injected UV signals and assessing performance under high-luminosity projections.

Main Results:

• The genetic algorithm successfully recovered relevant operator subsets.
• The method identified directions in parameter space most likely to exhibit deviations.
• The SMEFT-based approach demonstrated enhanced sensitivity for new physics discovery.
• The strategy proved effective in detecting scenarios that outperform the Standard Model.

Conclusions:

• The developed SMEFT-based strategy offers a powerful tool for new physics searches.
• Model selection-driven discovery searches provide a scalable framework for future collider data analyses.
• This approach enhances the potential for uncovering indirect signs of physics beyond the Standard Model.