What are the most common dark matter compatibility tests performed on the Landscape Square table?

The Landscape Square table provides a structured framework for comparing dark matter model predictions against empirical data. The most common compatibility tests performed within this framework fall into three primary categories. First, direct detection tests scrutinize models for potential signals from dark matter particles scattering off nuclei in underground detectors, such as XENONnT or LZ. Predictions for scattering cross-sections are compared against the ever-tightening experimental null results. Second, indirect detection tests analyze models for signatures of dark matter annihilation or decay products, like gamma-rays, neutrinos, or cosmic rays. Data from telescopes like Fermi-LAT or observatories like IceCube are used to constrain these predicted fluxes. Finally, astrophysical and cosmological constraints are applied. This includes ensuring the model's predicted relic abundance aligns with Planck satellite measurements of the cosmic microwave background and that its properties do not disrupt the formation of large-scale structures or stellar dynamics within galaxies. The Landscape Square table facilitates a systematic side-by-side evaluation of how different theoretical models fare against this multifaceted battery of tests, highlighting viable parameter spaces and ruling out incompatible candidates.