Domain Adaptation for Simulation-Based Dark Matter Searches Using Strong Gravitational Lensing

Understand

Clues to the identity of dark matter have remained surprisingly elusive, given the scope of experimental programs aimed at its identification.

• While terrestrial experiments may be able to nail down a model, an alternative, and equally promising, method is to identify dark matter based on astrophysical or cosmological signatures.
• A particularly sensitive approach is based on the unique signature of dark matter substructure on galaxy-galaxy strong lensing images.
• Machine learning applications have been explored in detail for extracting just this signal.