Generic Predictions for Primordial Perturbations and Their Implications

We introduce a novel framework for studying small-scale primordialperturbations and their cosmological implications. The framework uses a deepreinforcement learning to generate scalar power spectrum profiles that areconsistent with current observational constraints. The framework is shown topredict the abundance of primordial black holes and the production of secondaryinduced gravitational waves. We demonstrate that the set up under considerationis capable of generating predictions that are beyond the traditionalmodel-based approaches.