Assessing Near-Future Direct Dark Matter Searches with Benchmark-Free Forecasting

Area of Science:

• Particle Physics
• Cosmology
• Astrophysics

Background:

• Forecasting dark matter (DM) search signal discrimination often relies on arbitrary benchmark points.
• This limits the exhaustive exploration of diverse DM models and their phenomenological distinctiveness.

Purpose of the Study:

• Introduce novel benchmark-free forecasting methods for DM searches.
• Enable exhaustive exploration and visualization of DM model distinctiveness using standard hypothesis testing.
• Reassess the signal discrimination power of future liquid xenon and argon direct DM searches.

Main Methods:

• Developed benchmark-free forecasting techniques based on standard hypothesis testing.
• Applied these methods to analyze future liquid xenon and argon direct detection experiments.
• Quantified parameter regions for discriminating various non-relativistic effective operators, millicharged DM, and magnetic dipole DM models.

Main Results:

• The inclusion of an argon target significantly enhances the prospects for reconstructing DM properties.
• Simultaneous discrimination of DM-nucleon interaction and DM mass is achievable only in a narrow parameter region (20-100 GeV DM mass, cross sections near current bounds).
• Future xenon and argon detectors can determine either DM mass or interaction type in most other regions, but not both.

Conclusions:

• Benchmark-free forecasting provides a more comprehensive approach to evaluating DM search capabilities.
• Argon detectors offer crucial advantages for detailed DM property determination.
• Current and near-future direct detection experiments face limitations in simultaneously characterizing both DM mass and interaction properties across broad parameter spaces.